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diff --git a/data/faction-picker-bot/models/alchemists_model.txt b/data/faction-picker-bot/models/alchemists_model.txt
new file mode 100644
index 0000000000000000000000000000000000000000..43625413d8d4a5c97f69065c40af8de27a07892b
--- /dev/null
+++ b/data/faction-picker-bot/models/alchemists_model.txt
@@ -0,0 +1,961 @@
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+[force_row_wise: 0]
+[histogram_pool_size: -1]
+[max_depth: -1]
+[min_data_in_leaf: 20]
+[min_sum_hessian_in_leaf: 0.001]
+[bagging_fraction: 1]
+[pos_bagging_fraction: 1]
+[neg_bagging_fraction: 1]
+[bagging_freq: 0]
+[bagging_seed: 3]
+[feature_fraction: 0.7]
+[feature_fraction_bynode: 1]
+[feature_fraction_seed: 2]
+[extra_trees: 0]
+[extra_seed: 6]
+[early_stopping_round: 10]
+[first_metric_only: 0]
+[max_delta_step: 0]
+[lambda_l1: 0]
+[lambda_l2: 0]
+[linear_lambda: 0]
+[min_gain_to_split: 0]
+[drop_rate: 0.1]
+[max_drop: 50]
+[skip_drop: 0.5]
+[xgboost_dart_mode: 0]
+[uniform_drop: 0]
+[drop_seed: 4]
+[top_rate: 0.2]
+[other_rate: 0.1]
+[min_data_per_group: 100]
+[max_cat_threshold: 32]
+[cat_l2: 10]
+[cat_smooth: 10]
+[max_cat_to_onehot: 4]
+[top_k: 20]
+[monotone_constraints: ]
+[monotone_constraints_method: basic]
+[monotone_penalty: 0]
+[feature_contri: ]
+[forcedsplits_filename: ]
+[refit_decay_rate: 0.9]
+[cegb_tradeoff: 1]
+[cegb_penalty_split: 0]
+[cegb_penalty_feature_lazy: ]
+[cegb_penalty_feature_coupled: ]
+[path_smooth: 0]
+[interaction_constraints: ]
+[verbosity: 1]
+[saved_feature_importance_type: 0]
+[max_bin: 255]
+[max_bin_by_feature: ]
+[min_data_in_bin: 3]
+[bin_construct_sample_cnt: 200000]
+[data_random_seed: 1]
+[is_enable_sparse: 1]
+[enable_bundle: 1]
+[use_missing: 1]
+[zero_as_missing: 0]
+[feature_pre_filter: 1]
+[pre_partition: 0]
+[two_round: 0]
+[header: 0]
+[label_column: ]
+[weight_column: ]
+[group_column: ]
+[ignore_column: ]
+[categorical_feature: ]
+[forcedbins_filename: ]
+[objective_seed: 5]
+[num_class: 1]
+[is_unbalance: 0]
+[scale_pos_weight: 1]
+[sigmoid: 1]
+[boost_from_average: 1]
+[reg_sqrt: 0]
+[alpha: 0.9]
+[fair_c: 1]
+[poisson_max_delta_step: 0.7]
+[tweedie_variance_power: 1.5]
+[lambdarank_truncation_level: 30]
+[lambdarank_norm: 1]
+[label_gain: ]
+[eval_at: ]
+[multi_error_top_k: 1]
+[auc_mu_weights: ]
+[num_machines: 1]
+[local_listen_port: 12400]
+[time_out: 120]
+[machine_list_filename: ]
+[machines: ]
+[gpu_platform_id: -1]
+[gpu_device_id: -1]
+[gpu_use_dp: 0]
+[num_gpu: 1]
+
+end of parameters
+
+pandas_categorical:[]
diff --git a/data/faction-picker-bot/models/auren_model.txt b/data/faction-picker-bot/models/auren_model.txt
new file mode 100644
index 0000000000000000000000000000000000000000..cdd28ea60ff2aac84f4b7a1f83e0b89b844d2696
--- /dev/null
+++ b/data/faction-picker-bot/models/auren_model.txt
@@ -0,0 +1,960 @@
+tree
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+feature_infos=[0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] none [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:3] [0:1] [0:1] none [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1]
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+
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+
+
+end of trees
+
+feature_importances:
+red=56
+BON3=51
+black=51
+brown=51
+BON2=49
+BON5=47
+BON8=47
+BON1=45
+BON4=44
+yellow=44
+grey=43
+BON9=41
+BON7=39
+no_players=30
+BON6=29
+x4_SCORE3=24
+blue=23
+x1_SCORE1=22
+x0_SCORE4=17
+x1_SCORE7=17
+x2_SCORE3=17
+BON10=17
+x4_SCORE7=15
+x0_SCORE5=14
+x2_SCORE1=14
+x1_SCORE3=12
+x3_SCORE7=12
+x3_SCORE8=12
+x1_SCORE9=11
+x2_SCORE6=11
+x4_SCORE6=11
+x5_SCORE6=11
+x0_SCORE7=10
+x0_SCORE8=10
+x1_SCORE4=10
+x2_SCORE5=10
+x3_SCORE5=10
+x5_SCORE8=10
+x0_SCORE1=9
+x2_SCORE9=9
+x3_SCORE4=9
+x5_SCORE4=9
+x0_map2=9
+x0_map3=9
+x2_SCORE4=8
+x2_SCORE7=8
+x3_SCORE1=8
+x3_SCORE2=8
+x4_SCORE5=8
+x4_SCORE8=8
+x5_SCORE3=8
+x5_SCORE5=8
+x2_SCORE8=7
+x3_SCORE3=7
+x5_SCORE7=7
+x0_SCORE2=6
+x0_SCORE6=6
+x3_SCORE6=6
+x4_SCORE4=6
+x5_SCORE2=6
+x1_SCORE2=5
+x4_SCORE2=5
+x5_SCORE9=5
+x0_map1=5
+x0_SCORE3=4
+x1_SCORE8=4
+x2_SCORE2=4
+x1_SCORE5=3
+x1_SCORE6=3
+x3_SCORE9=3
+x4_SCORE9=3
+
+parameters:
+[boosting: dart]
+[objective: regression]
+[metric: l2]
+[tree_learner: serial]
+[device_type: cpu]
+[linear_tree: 0]
+[data: ]
+[valid: ]
+[num_iterations: 40]
+[learning_rate: 0.1]
+[num_leaves: 31]
+[num_threads: 0]
+[deterministic: 0]
+[force_col_wise: 0]
+[force_row_wise: 0]
+[histogram_pool_size: -1]
+[max_depth: -1]
+[min_data_in_leaf: 20]
+[min_sum_hessian_in_leaf: 0.001]
+[bagging_fraction: 1]
+[pos_bagging_fraction: 1]
+[neg_bagging_fraction: 1]
+[bagging_freq: 0]
+[bagging_seed: 3]
+[feature_fraction: 0.7]
+[feature_fraction_bynode: 1]
+[feature_fraction_seed: 2]
+[extra_trees: 0]
+[extra_seed: 6]
+[early_stopping_round: 10]
+[first_metric_only: 0]
+[max_delta_step: 0]
+[lambda_l1: 0]
+[lambda_l2: 0]
+[linear_lambda: 0]
+[min_gain_to_split: 0]
+[drop_rate: 0.1]
+[max_drop: 50]
+[skip_drop: 0.5]
+[xgboost_dart_mode: 0]
+[uniform_drop: 0]
+[drop_seed: 4]
+[top_rate: 0.2]
+[other_rate: 0.1]
+[min_data_per_group: 100]
+[max_cat_threshold: 32]
+[cat_l2: 10]
+[cat_smooth: 10]
+[max_cat_to_onehot: 4]
+[top_k: 20]
+[monotone_constraints: ]
+[monotone_constraints_method: basic]
+[monotone_penalty: 0]
+[feature_contri: ]
+[forcedsplits_filename: ]
+[refit_decay_rate: 0.9]
+[cegb_tradeoff: 1]
+[cegb_penalty_split: 0]
+[cegb_penalty_feature_lazy: ]
+[cegb_penalty_feature_coupled: ]
+[path_smooth: 0]
+[interaction_constraints: ]
+[verbosity: 1]
+[saved_feature_importance_type: 0]
+[max_bin: 255]
+[max_bin_by_feature: ]
+[min_data_in_bin: 3]
+[bin_construct_sample_cnt: 200000]
+[data_random_seed: 1]
+[is_enable_sparse: 1]
+[enable_bundle: 1]
+[use_missing: 1]
+[zero_as_missing: 0]
+[feature_pre_filter: 1]
+[pre_partition: 0]
+[two_round: 0]
+[header: 0]
+[label_column: ]
+[weight_column: ]
+[group_column: ]
+[ignore_column: ]
+[categorical_feature: ]
+[forcedbins_filename: ]
+[objective_seed: 5]
+[num_class: 1]
+[is_unbalance: 0]
+[scale_pos_weight: 1]
+[sigmoid: 1]
+[boost_from_average: 1]
+[reg_sqrt: 0]
+[alpha: 0.9]
+[fair_c: 1]
+[poisson_max_delta_step: 0.7]
+[tweedie_variance_power: 1.5]
+[lambdarank_truncation_level: 30]
+[lambdarank_norm: 1]
+[label_gain: ]
+[eval_at: ]
+[multi_error_top_k: 1]
+[auc_mu_weights: ]
+[num_machines: 1]
+[local_listen_port: 12400]
+[time_out: 120]
+[machine_list_filename: ]
+[machines: ]
+[gpu_platform_id: -1]
+[gpu_device_id: -1]
+[gpu_use_dp: 0]
+[num_gpu: 1]
+
+end of parameters
+
+pandas_categorical:[]
diff --git a/data/faction-picker-bot/models/chaosmagicians_model.txt b/data/faction-picker-bot/models/chaosmagicians_model.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4e7e68bee588be3744b696395a992fdb0b326a26
--- /dev/null
+++ b/data/faction-picker-bot/models/chaosmagicians_model.txt
@@ -0,0 +1,961 @@
+tree
+version=v3
+num_class=1
+num_tree_per_iteration=1
+label_index=0
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+
+
+end of trees
+
+feature_importances:
+BON4=77
+BON2=76
+BON3=73
+BON8=69
+BON7=60
+brown=60
+blue=57
+BON9=55
+BON5=50
+yellow=49
+green=46
+BON10=40
+grey=36
+black=32
+BON1=29
+no_players=17
+x0_SCORE8=16
+x1_SCORE3=13
+x0_map1=12
+x5_SCORE6=11
+x2_SCORE1=10
+x3_SCORE1=10
+x5_SCORE8=10
+x2_SCORE5=9
+x2_SCORE7=9
+x4_SCORE2=9
+x5_SCORE4=9
+x0_SCORE2=8
+x0_SCORE6=8
+x2_SCORE9=8
+x4_SCORE3=8
+x4_SCORE6=8
+x5_SCORE3=8
+x5_SCORE7=8
+BON6=8
+x0_map2=8
+x0_SCORE1=7
+x0_SCORE7=7
+x1_SCORE8=7
+x2_SCORE3=7
+x2_SCORE8=7
+x3_SCORE2=7
+x3_SCORE6=7
+x3_SCORE9=7
+x4_SCORE7=7
+x0_map3=7
+x0_SCORE4=6
+x1_SCORE5=6
+x1_SCORE6=6
+x2_SCORE2=6
+x2_SCORE4=6
+x2_SCORE6=6
+x0_SCORE5=5
+x3_SCORE3=5
+x3_SCORE4=5
+x4_SCORE4=5
+x4_SCORE5=5
+x4_SCORE8=5
+x5_SCORE5=5
+x1_SCORE2=4
+x1_SCORE4=4
+x1_SCORE7=4
+x3_SCORE5=4
+x3_SCORE7=4
+x4_SCORE9=4
+x5_SCORE2=4
+x0_SCORE3=3
+x0_SCORE9=3
+x1_SCORE9=3
+x5_SCORE9=3
+x1_SCORE1=2
+x3_SCORE8=1
+
+parameters:
+[boosting: dart]
+[objective: regression]
+[metric: l2]
+[tree_learner: serial]
+[device_type: cpu]
+[linear_tree: 0]
+[data: ]
+[valid: ]
+[num_iterations: 40]
+[learning_rate: 0.1]
+[num_leaves: 31]
+[num_threads: 0]
+[deterministic: 0]
+[force_col_wise: 0]
+[force_row_wise: 0]
+[histogram_pool_size: -1]
+[max_depth: -1]
+[min_data_in_leaf: 20]
+[min_sum_hessian_in_leaf: 0.001]
+[bagging_fraction: 1]
+[pos_bagging_fraction: 1]
+[neg_bagging_fraction: 1]
+[bagging_freq: 0]
+[bagging_seed: 3]
+[feature_fraction: 0.7]
+[feature_fraction_bynode: 1]
+[feature_fraction_seed: 2]
+[extra_trees: 0]
+[extra_seed: 6]
+[early_stopping_round: 10]
+[first_metric_only: 0]
+[max_delta_step: 0]
+[lambda_l1: 0]
+[lambda_l2: 0]
+[linear_lambda: 0]
+[min_gain_to_split: 0]
+[drop_rate: 0.1]
+[max_drop: 50]
+[skip_drop: 0.5]
+[xgboost_dart_mode: 0]
+[uniform_drop: 0]
+[drop_seed: 4]
+[top_rate: 0.2]
+[other_rate: 0.1]
+[min_data_per_group: 100]
+[max_cat_threshold: 32]
+[cat_l2: 10]
+[cat_smooth: 10]
+[max_cat_to_onehot: 4]
+[top_k: 20]
+[monotone_constraints: ]
+[monotone_constraints_method: basic]
+[monotone_penalty: 0]
+[feature_contri: ]
+[forcedsplits_filename: ]
+[refit_decay_rate: 0.9]
+[cegb_tradeoff: 1]
+[cegb_penalty_split: 0]
+[cegb_penalty_feature_lazy: ]
+[cegb_penalty_feature_coupled: ]
+[path_smooth: 0]
+[interaction_constraints: ]
+[verbosity: 1]
+[saved_feature_importance_type: 0]
+[max_bin: 255]
+[max_bin_by_feature: ]
+[min_data_in_bin: 3]
+[bin_construct_sample_cnt: 200000]
+[data_random_seed: 1]
+[is_enable_sparse: 1]
+[enable_bundle: 1]
+[use_missing: 1]
+[zero_as_missing: 0]
+[feature_pre_filter: 1]
+[pre_partition: 0]
+[two_round: 0]
+[header: 0]
+[label_column: ]
+[weight_column: ]
+[group_column: ]
+[ignore_column: ]
+[categorical_feature: ]
+[forcedbins_filename: ]
+[objective_seed: 5]
+[num_class: 1]
+[is_unbalance: 0]
+[scale_pos_weight: 1]
+[sigmoid: 1]
+[boost_from_average: 1]
+[reg_sqrt: 0]
+[alpha: 0.9]
+[fair_c: 1]
+[poisson_max_delta_step: 0.7]
+[tweedie_variance_power: 1.5]
+[lambdarank_truncation_level: 30]
+[lambdarank_norm: 1]
+[label_gain: ]
+[eval_at: ]
+[multi_error_top_k: 1]
+[auc_mu_weights: ]
+[num_machines: 1]
+[local_listen_port: 12400]
+[time_out: 120]
+[machine_list_filename: ]
+[machines: ]
+[gpu_platform_id: -1]
+[gpu_device_id: -1]
+[gpu_use_dp: 0]
+[num_gpu: 1]
+
+end of parameters
+
+pandas_categorical:[]
diff --git a/data/faction-picker-bot/models/cultists_model.txt b/data/faction-picker-bot/models/cultists_model.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2fcadbeeaa1b8868b277c3be38227212fb76969b
--- /dev/null
+++ b/data/faction-picker-bot/models/cultists_model.txt
@@ -0,0 +1,961 @@
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+Tree=36
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+
+Tree=37
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+
+Tree=38
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+
+Tree=39
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+is_linear=0
+shrinkage=0.05
+
+
+end of trees
+
+feature_importances:
+red=73
+BON8=66
+BON4=63
+blue=60
+BON10=54
+black=51
+BON5=47
+green=40
+no_players=38
+grey=35
+yellow=35
+BON1=33
+BON2=29
+x5_SCORE7=22
+x4_SCORE8=21
+BON6=21
+x0_SCORE7=16
+x5_SCORE8=16
+x0_map1=16
+BON7=15
+x3_SCORE4=14
+x5_SCORE2=14
+x5_SCORE6=14
+x1_SCORE8=13
+x3_SCORE8=13
+x4_SCORE5=13
+x0_SCORE1=12
+x0_SCORE3=12
+x1_SCORE1=12
+x1_SCORE3=12
+x5_SCORE3=12
+x0_map2=12
+x0_SCORE4=11
+x2_SCORE2=11
+x3_SCORE2=11
+x4_SCORE6=11
+x4_SCORE7=11
+BON3=11
+BON9=11
+x0_map3=11
+x0_SCORE2=9
+x0_SCORE6=9
+x1_SCORE5=9
+x1_SCORE7=9
+x2_SCORE4=9
+x2_SCORE6=9
+x2_SCORE9=9
+x3_SCORE1=9
+x4_SCORE3=9
+x4_SCORE9=9
+x5_SCORE4=9
+x1_SCORE4=8
+x3_SCORE5=8
+x4_SCORE2=8
+x5_SCORE9=8
+x3_SCORE3=7
+x4_SCORE4=7
+x1_SCORE6=6
+x2_SCORE7=6
+x0_SCORE5=5
+x1_SCORE9=5
+x2_SCORE8=5
+x3_SCORE6=5
+x3_SCORE7=5
+x5_SCORE5=5
+x1_SCORE2=4
+x2_SCORE1=4
+x2_SCORE5=4
+x0_SCORE9=3
+x0_SCORE8=2
+x2_SCORE3=2
+x3_SCORE9=2
+
+parameters:
+[boosting: dart]
+[objective: regression]
+[metric: l2]
+[tree_learner: serial]
+[device_type: cpu]
+[linear_tree: 0]
+[data: ]
+[valid: ]
+[num_iterations: 40]
+[learning_rate: 0.1]
+[num_leaves: 31]
+[num_threads: 0]
+[deterministic: 0]
+[force_col_wise: 0]
+[force_row_wise: 0]
+[histogram_pool_size: -1]
+[max_depth: -1]
+[min_data_in_leaf: 20]
+[min_sum_hessian_in_leaf: 0.001]
+[bagging_fraction: 1]
+[pos_bagging_fraction: 1]
+[neg_bagging_fraction: 1]
+[bagging_freq: 0]
+[bagging_seed: 3]
+[feature_fraction: 0.7]
+[feature_fraction_bynode: 1]
+[feature_fraction_seed: 2]
+[extra_trees: 0]
+[extra_seed: 6]
+[early_stopping_round: 10]
+[first_metric_only: 0]
+[max_delta_step: 0]
+[lambda_l1: 0]
+[lambda_l2: 0]
+[linear_lambda: 0]
+[min_gain_to_split: 0]
+[drop_rate: 0.1]
+[max_drop: 50]
+[skip_drop: 0.5]
+[xgboost_dart_mode: 0]
+[uniform_drop: 0]
+[drop_seed: 4]
+[top_rate: 0.2]
+[other_rate: 0.1]
+[min_data_per_group: 100]
+[max_cat_threshold: 32]
+[cat_l2: 10]
+[cat_smooth: 10]
+[max_cat_to_onehot: 4]
+[top_k: 20]
+[monotone_constraints: ]
+[monotone_constraints_method: basic]
+[monotone_penalty: 0]
+[feature_contri: ]
+[forcedsplits_filename: ]
+[refit_decay_rate: 0.9]
+[cegb_tradeoff: 1]
+[cegb_penalty_split: 0]
+[cegb_penalty_feature_lazy: ]
+[cegb_penalty_feature_coupled: ]
+[path_smooth: 0]
+[interaction_constraints: ]
+[verbosity: 1]
+[saved_feature_importance_type: 0]
+[max_bin: 255]
+[max_bin_by_feature: ]
+[min_data_in_bin: 3]
+[bin_construct_sample_cnt: 200000]
+[data_random_seed: 1]
+[is_enable_sparse: 1]
+[enable_bundle: 1]
+[use_missing: 1]
+[zero_as_missing: 0]
+[feature_pre_filter: 1]
+[pre_partition: 0]
+[two_round: 0]
+[header: 0]
+[label_column: ]
+[weight_column: ]
+[group_column: ]
+[ignore_column: ]
+[categorical_feature: ]
+[forcedbins_filename: ]
+[objective_seed: 5]
+[num_class: 1]
+[is_unbalance: 0]
+[scale_pos_weight: 1]
+[sigmoid: 1]
+[boost_from_average: 1]
+[reg_sqrt: 0]
+[alpha: 0.9]
+[fair_c: 1]
+[poisson_max_delta_step: 0.7]
+[tweedie_variance_power: 1.5]
+[lambdarank_truncation_level: 30]
+[lambdarank_norm: 1]
+[label_gain: ]
+[eval_at: ]
+[multi_error_top_k: 1]
+[auc_mu_weights: ]
+[num_machines: 1]
+[local_listen_port: 12400]
+[time_out: 120]
+[machine_list_filename: ]
+[machines: ]
+[gpu_platform_id: -1]
+[gpu_device_id: -1]
+[gpu_use_dp: 0]
+[num_gpu: 1]
+
+end of parameters
+
+pandas_categorical:[]
diff --git a/data/faction-picker-bot/models/darklings_model.txt b/data/faction-picker-bot/models/darklings_model.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2f8212b737db6fcf28deecd979616154807b8cd9
--- /dev/null
+++ b/data/faction-picker-bot/models/darklings_model.txt
@@ -0,0 +1,961 @@
+tree
+version=v3
+num_class=1
+num_tree_per_iteration=1
+label_index=0
+max_feature_idx=73
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+
+
+end of trees
+
+feature_importances:
+yellow=88
+red=71
+green=70
+BON2=69
+brown=68
+BON5=65
+BON4=62
+BON8=55
+BON1=50
+grey=50
+BON7=37
+no_players=33
+BON3=31
+BON10=30
+blue=24
+x5_SCORE8=21
+BON6=20
+x0_SCORE2=16
+x0_map1=16
+BON9=15
+x0_map2=14
+x0_SCORE7=13
+x2_SCORE2=13
+x5_SCORE5=12
+x2_SCORE1=11
+x1_SCORE7=10
+x2_SCORE3=10
+x3_SCORE7=10
+x0_SCORE6=9
+x5_SCORE7=9
+x3_SCORE1=8
+x0_SCORE1=7
+x1_SCORE2=7
+x1_SCORE4=7
+x2_SCORE5=7
+x3_SCORE4=7
+x3_SCORE5=7
+x4_SCORE5=7
+x5_SCORE2=7
+x5_SCORE3=7
+x1_SCORE3=6
+x4_SCORE3=6
+x4_SCORE8=6
+x5_SCORE6=6
+x5_SCORE9=6
+x0_SCORE9=5
+x1_SCORE1=5
+x1_SCORE9=5
+x2_SCORE7=5
+x3_SCORE6=5
+x3_SCORE8=5
+x4_SCORE2=5
+x4_SCORE7=5
+x0_SCORE3=4
+x0_SCORE4=4
+x0_SCORE8=4
+x1_SCORE8=4
+x2_SCORE4=4
+x2_SCORE8=4
+x4_SCORE4=4
+x0_SCORE5=3
+x2_SCORE9=3
+x3_SCORE3=3
+x3_SCORE9=3
+x4_SCORE6=3
+x5_SCORE4=3
+x0_map3=3
+x1_SCORE6=2
+x2_SCORE6=2
+x3_SCORE2=2
+x1_SCORE5=1
+x4_SCORE9=1
+
+parameters:
+[boosting: dart]
+[objective: regression]
+[metric: l2]
+[tree_learner: serial]
+[device_type: cpu]
+[linear_tree: 0]
+[data: ]
+[valid: ]
+[num_iterations: 40]
+[learning_rate: 0.1]
+[num_leaves: 31]
+[num_threads: 0]
+[deterministic: 0]
+[force_col_wise: 0]
+[force_row_wise: 0]
+[histogram_pool_size: -1]
+[max_depth: -1]
+[min_data_in_leaf: 20]
+[min_sum_hessian_in_leaf: 0.001]
+[bagging_fraction: 1]
+[pos_bagging_fraction: 1]
+[neg_bagging_fraction: 1]
+[bagging_freq: 0]
+[bagging_seed: 3]
+[feature_fraction: 0.7]
+[feature_fraction_bynode: 1]
+[feature_fraction_seed: 2]
+[extra_trees: 0]
+[extra_seed: 6]
+[early_stopping_round: 10]
+[first_metric_only: 0]
+[max_delta_step: 0]
+[lambda_l1: 0]
+[lambda_l2: 0]
+[linear_lambda: 0]
+[min_gain_to_split: 0]
+[drop_rate: 0.1]
+[max_drop: 50]
+[skip_drop: 0.5]
+[xgboost_dart_mode: 0]
+[uniform_drop: 0]
+[drop_seed: 4]
+[top_rate: 0.2]
+[other_rate: 0.1]
+[min_data_per_group: 100]
+[max_cat_threshold: 32]
+[cat_l2: 10]
+[cat_smooth: 10]
+[max_cat_to_onehot: 4]
+[top_k: 20]
+[monotone_constraints: ]
+[monotone_constraints_method: basic]
+[monotone_penalty: 0]
+[feature_contri: ]
+[forcedsplits_filename: ]
+[refit_decay_rate: 0.9]
+[cegb_tradeoff: 1]
+[cegb_penalty_split: 0]
+[cegb_penalty_feature_lazy: ]
+[cegb_penalty_feature_coupled: ]
+[path_smooth: 0]
+[interaction_constraints: ]
+[verbosity: 1]
+[saved_feature_importance_type: 0]
+[max_bin: 255]
+[max_bin_by_feature: ]
+[min_data_in_bin: 3]
+[bin_construct_sample_cnt: 200000]
+[data_random_seed: 1]
+[is_enable_sparse: 1]
+[enable_bundle: 1]
+[use_missing: 1]
+[zero_as_missing: 0]
+[feature_pre_filter: 1]
+[pre_partition: 0]
+[two_round: 0]
+[header: 0]
+[label_column: ]
+[weight_column: ]
+[group_column: ]
+[ignore_column: ]
+[categorical_feature: ]
+[forcedbins_filename: ]
+[objective_seed: 5]
+[num_class: 1]
+[is_unbalance: 0]
+[scale_pos_weight: 1]
+[sigmoid: 1]
+[boost_from_average: 1]
+[reg_sqrt: 0]
+[alpha: 0.9]
+[fair_c: 1]
+[poisson_max_delta_step: 0.7]
+[tweedie_variance_power: 1.5]
+[lambdarank_truncation_level: 30]
+[lambdarank_norm: 1]
+[label_gain: ]
+[eval_at: ]
+[multi_error_top_k: 1]
+[auc_mu_weights: ]
+[num_machines: 1]
+[local_listen_port: 12400]
+[time_out: 120]
+[machine_list_filename: ]
+[machines: ]
+[gpu_platform_id: -1]
+[gpu_device_id: -1]
+[gpu_use_dp: 0]
+[num_gpu: 1]
+
+end of parameters
+
+pandas_categorical:[]
diff --git a/data/faction-picker-bot/models/dwarves_model.txt b/data/faction-picker-bot/models/dwarves_model.txt
new file mode 100644
index 0000000000000000000000000000000000000000..41b86d1ef0d3fae4cdc4e219a04de9d98e7177e4
--- /dev/null
+++ b/data/faction-picker-bot/models/dwarves_model.txt
@@ -0,0 +1,961 @@
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+left_child=1 2 12 -3 5 -4 8 9 -6 11 -10 -7 14 -14 27 16 17 -16 20 21 28 22 25 26 -5 -20 -22 -1 -17 -2
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+internal_weight=0 9565 3682 5883 1490 591 899 219 830 197 101 174 2192 247 1945 1353 525 444 828 412 416 354 317 253 3954 280 224 592 163 3031
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+is_linear=0
+shrinkage=0.1
+
+
+Tree=37
+num_leaves=31
+num_cat=0
+split_feature=60 55 3 20 65 20 50 10 62 51 51 18 0 65 32 69 69 22 5 59 63 59 33 0 26 10 28 40 45 25
+split_gain=4716.39 2959.91 4084.84 3751.28 3736.18 3678.48 3887.14 3749.58 3016.16 3342.05 3146.7 9975 4523.04 3509.33 3373.76 3047.3 2905.86 2829.4 2773.72 3515.24 2663.05 2795.44 3976.79 3437.38 3415.81 2638.38 2588.96 2584.24 3066.7 2788.85
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+decision_type=2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
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+
+Tree=38
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+split_gain=7727.64 5128.79 2683.25 2829.54 3718.65 3316.6 3622.14 2424.1 2503.79 2330.14 1952.11 2579.74 5504.9 2281.89 3293.83 2137.46 2101.18 2911.94 2542.64 2660.04 2486.04 2247.45 2592.67 1948.71 1827.03 1709.13 4069.71 3182.85 3123.18 3488.23
+threshold=1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.5000000000000002 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35
+decision_type=2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
+left_child=1 2 3 -1 5 -5 9 25 -9 -7 11 12 23 -12 15 -15 17 20 24 -20 -13 22 -22 -4 -19 28 -27 -28 29 -2
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+
+
+Tree=39
+num_leaves=31
+num_cat=0
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+split_gain=6183.1 3829.22 2632.96 2491.54 2871.76 2470.85 2329.26 3526.9 2435.02 2313.97 2280.13 2671.5 2743.45 2544.85 2189.99 2180.61 2162.01 3575.72 3025.18 2552.85 2235.48 2140.57 2100.34 2076.37 2043.59 1976.53 5000.71 2104.04 1961.69 1934.37
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+decision_type=2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
+left_child=1 2 10 4 21 -3 8 22 25 16 11 12 -1 14 23 24 20 18 -18 -20 -2 -4 -8 -12 -5 27 -27 -7 -14 -11
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+leaf_count=2720 922 2680 115 119 30 825 141 25 115 2612 144 390 135 26 33 38 22 21 38 54 135 24 29 26 144 100 51 91 275 516
+internal_value=0 0.0253098 0.0586611 0.170218 0.323855 -0.00937327 -0.0638099 0.0962945 -0.0902231 -0.048487 0.0446756 0.0546213 0.0699971 -0.108203 -0.048551 0.083957 0.0107922 -0.17763 -0.0671858 -0.193139 0.0348575 0.419608 0.0147499 -0.120907 0.135112 -0.113783 0.0538261 -0.141413 0.190567 -0.0710768
+internal_weight=0 8276 4219 470 169 4057 1377 195 1182 4320 3749 3520 3130 229 203 301 1192 135 114 92 1057 139 170 170 263 1067 151 916 410 3128
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+is_linear=0
+shrinkage=0.05
+
+
+end of trees
+
+feature_importances:
+yellow=64
+blue=56
+BON4=52
+BON2=47
+BON3=47
+BON8=44
+BON5=41
+no_players=38
+red=38
+BON10=37
+green=34
+black=33
+BON7=27
+BON6=25
+brown=25
+x1_SCORE8=21
+BON9=21
+x4_SCORE2=19
+x5_SCORE7=19
+x0_SCORE7=18
+x1_SCORE1=17
+x1_SCORE2=17
+x2_SCORE1=16
+x3_SCORE9=15
+x2_SCORE4=14
+x2_SCORE8=14
+x3_SCORE8=14
+x0_map2=14
+x3_SCORE5=13
+x5_SCORE9=13
+BON1=13
+x0_SCORE4=12
+x2_SCORE3=12
+x3_SCORE2=12
+x3_SCORE6=12
+x5_SCORE2=12
+x5_SCORE8=12
+x0_SCORE2=11
+x1_SCORE3=11
+x1_SCORE7=11
+x0_SCORE6=10
+x0_SCORE8=10
+x3_SCORE7=10
+x4_SCORE3=10
+x4_SCORE6=10
+x5_SCORE5=10
+x5_SCORE6=10
+x0_SCORE3=9
+x0_SCORE9=9
+x2_SCORE5=9
+x2_SCORE7=9
+x4_SCORE7=9
+x1_SCORE6=8
+x3_SCORE4=8
+x4_SCORE4=8
+x5_SCORE3=8
+x5_SCORE4=8
+x3_SCORE1=7
+x0_map3=7
+x0_SCORE1=6
+x0_map1=6
+x0_SCORE5=5
+x1_SCORE4=5
+x1_SCORE5=5
+x2_SCORE2=5
+x2_SCORE6=5
+x2_SCORE9=5
+x3_SCORE3=5
+x4_SCORE5=5
+x4_SCORE8=4
+x1_SCORE9=2
+x4_SCORE9=2
+
+parameters:
+[boosting: dart]
+[objective: regression]
+[metric: l2]
+[tree_learner: serial]
+[device_type: cpu]
+[linear_tree: 0]
+[data: ]
+[valid: ]
+[num_iterations: 40]
+[learning_rate: 0.1]
+[num_leaves: 31]
+[num_threads: 0]
+[deterministic: 0]
+[force_col_wise: 0]
+[force_row_wise: 0]
+[histogram_pool_size: -1]
+[max_depth: -1]
+[min_data_in_leaf: 20]
+[min_sum_hessian_in_leaf: 0.001]
+[bagging_fraction: 1]
+[pos_bagging_fraction: 1]
+[neg_bagging_fraction: 1]
+[bagging_freq: 0]
+[bagging_seed: 3]
+[feature_fraction: 0.7]
+[feature_fraction_bynode: 1]
+[feature_fraction_seed: 2]
+[extra_trees: 0]
+[extra_seed: 6]
+[early_stopping_round: 10]
+[first_metric_only: 0]
+[max_delta_step: 0]
+[lambda_l1: 0]
+[lambda_l2: 0]
+[linear_lambda: 0]
+[min_gain_to_split: 0]
+[drop_rate: 0.1]
+[max_drop: 50]
+[skip_drop: 0.5]
+[xgboost_dart_mode: 0]
+[uniform_drop: 0]
+[drop_seed: 4]
+[top_rate: 0.2]
+[other_rate: 0.1]
+[min_data_per_group: 100]
+[max_cat_threshold: 32]
+[cat_l2: 10]
+[cat_smooth: 10]
+[max_cat_to_onehot: 4]
+[top_k: 20]
+[monotone_constraints: ]
+[monotone_constraints_method: basic]
+[monotone_penalty: 0]
+[feature_contri: ]
+[forcedsplits_filename: ]
+[refit_decay_rate: 0.9]
+[cegb_tradeoff: 1]
+[cegb_penalty_split: 0]
+[cegb_penalty_feature_lazy: ]
+[cegb_penalty_feature_coupled: ]
+[path_smooth: 0]
+[interaction_constraints: ]
+[verbosity: 1]
+[saved_feature_importance_type: 0]
+[max_bin: 255]
+[max_bin_by_feature: ]
+[min_data_in_bin: 3]
+[bin_construct_sample_cnt: 200000]
+[data_random_seed: 1]
+[is_enable_sparse: 1]
+[enable_bundle: 1]
+[use_missing: 1]
+[zero_as_missing: 0]
+[feature_pre_filter: 1]
+[pre_partition: 0]
+[two_round: 0]
+[header: 0]
+[label_column: ]
+[weight_column: ]
+[group_column: ]
+[ignore_column: ]
+[categorical_feature: ]
+[forcedbins_filename: ]
+[objective_seed: 5]
+[num_class: 1]
+[is_unbalance: 0]
+[scale_pos_weight: 1]
+[sigmoid: 1]
+[boost_from_average: 1]
+[reg_sqrt: 0]
+[alpha: 0.9]
+[fair_c: 1]
+[poisson_max_delta_step: 0.7]
+[tweedie_variance_power: 1.5]
+[lambdarank_truncation_level: 30]
+[lambdarank_norm: 1]
+[label_gain: ]
+[eval_at: ]
+[multi_error_top_k: 1]
+[auc_mu_weights: ]
+[num_machines: 1]
+[local_listen_port: 12400]
+[time_out: 120]
+[machine_list_filename: ]
+[machines: ]
+[gpu_platform_id: -1]
+[gpu_device_id: -1]
+[gpu_use_dp: 0]
+[num_gpu: 1]
+
+end of parameters
+
+pandas_categorical:[]
diff --git a/data/faction-picker-bot/models/engineers_model.txt b/data/faction-picker-bot/models/engineers_model.txt
new file mode 100644
index 0000000000000000000000000000000000000000..46b249415bc28a717cbf4e700a63ae1c9feac76f
--- /dev/null
+++ b/data/faction-picker-bot/models/engineers_model.txt
@@ -0,0 +1,961 @@
+tree
+version=v3
+num_class=1
+num_tree_per_iteration=1
+label_index=0
+max_feature_idx=73
+objective=regression
+feature_names=x0_SCORE1 x0_SCORE2 x0_SCORE3 x0_SCORE4 x0_SCORE5 x0_SCORE6 x0_SCORE7 x0_SCORE8 x0_SCORE9 x1_SCORE1 x1_SCORE2 x1_SCORE3 x1_SCORE4 x1_SCORE5 x1_SCORE6 x1_SCORE7 x1_SCORE8 x1_SCORE9 x2_SCORE1 x2_SCORE2 x2_SCORE3 x2_SCORE4 x2_SCORE5 x2_SCORE6 x2_SCORE7 x2_SCORE8 x2_SCORE9 x3_SCORE1 x3_SCORE2 x3_SCORE3 x3_SCORE4 x3_SCORE5 x3_SCORE6 x3_SCORE7 x3_SCORE8 x3_SCORE9 x4_SCORE1 x4_SCORE2 x4_SCORE3 x4_SCORE4 x4_SCORE5 x4_SCORE6 x4_SCORE7 x4_SCORE8 x4_SCORE9 x5_SCORE2 x5_SCORE3 x5_SCORE4 x5_SCORE5 x5_SCORE6 x5_SCORE7 x5_SCORE8 x5_SCORE9 BON1 BON2 BON3 BON4 BON5 BON6 BON7 BON8 BON9 BON10 no_players red blue green black grey yellow brown x0_map1 x0_map2 x0_map3
+feature_infos=[0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] none [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:3] [0:1] [0:1] [0:1] [0:1] none [0:1] [0:1] [0:1] [0:1] [0:1]
+tree_sizes=2839 2953 2923 2933 2929 2929 2912 2957 2920 2918 2950 2943 2918 2937 2905 2921 2920 2918 2902 2944 2967 2919 2923 2925 2954 2912 2898 2951 2899 2921 2929 2918 2942 2915 2944 2964 2902 2900 2898 2937
+
+Tree=0
+num_leaves=31
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+
+Tree=37
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+Tree=38
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+
+Tree=39
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+is_linear=0
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+
+
+end of trees
+
+feature_importances:
+BON3=64
+yellow=64
+BON8=63
+BON2=61
+BON9=60
+black=59
+blue=51
+green=47
+BON4=43
+BON5=40
+no_players=40
+brown=34
+BON7=32
+BON10=31
+red=30
+x1_SCORE6=17
+x2_SCORE8=17
+x3_SCORE8=17
+BON1=17
+x0_SCORE2=16
+x4_SCORE7=16
+x5_SCORE3=15
+x0_SCORE4=14
+x1_SCORE1=14
+x0_map2=14
+x2_SCORE2=13
+x0_map1=13
+x5_SCORE8=11
+BON6=11
+x0_SCORE5=10
+x0_SCORE7=10
+x1_SCORE2=10
+x2_SCORE3=10
+x2_SCORE4=10
+x3_SCORE5=10
+x0_map3=9
+x0_SCORE8=8
+x1_SCORE4=8
+x2_SCORE6=8
+x2_SCORE7=8
+x4_SCORE3=8
+x4_SCORE6=8
+x5_SCORE9=8
+x0_SCORE1=7
+x0_SCORE3=7
+x1_SCORE8=7
+x2_SCORE5=7
+x4_SCORE2=7
+x4_SCORE4=7
+x5_SCORE2=7
+x5_SCORE5=7
+x2_SCORE1=6
+x3_SCORE2=6
+x4_SCORE5=6
+x4_SCORE9=6
+x5_SCORE4=6
+x5_SCORE6=6
+x0_SCORE6=5
+x3_SCORE1=5
+x3_SCORE6=5
+x3_SCORE9=5
+x4_SCORE8=5
+x0_SCORE9=4
+x1_SCORE3=4
+x1_SCORE7=4
+x2_SCORE9=4
+x1_SCORE5=3
+x1_SCORE9=3
+x3_SCORE3=3
+x3_SCORE4=3
+x3_SCORE7=3
+x5_SCORE7=3
+
+parameters:
+[boosting: dart]
+[objective: regression]
+[metric: l2]
+[tree_learner: serial]
+[device_type: cpu]
+[linear_tree: 0]
+[data: ]
+[valid: ]
+[num_iterations: 40]
+[learning_rate: 0.1]
+[num_leaves: 31]
+[num_threads: 0]
+[deterministic: 0]
+[force_col_wise: 0]
+[force_row_wise: 0]
+[histogram_pool_size: -1]
+[max_depth: -1]
+[min_data_in_leaf: 20]
+[min_sum_hessian_in_leaf: 0.001]
+[bagging_fraction: 1]
+[pos_bagging_fraction: 1]
+[neg_bagging_fraction: 1]
+[bagging_freq: 0]
+[bagging_seed: 3]
+[feature_fraction: 0.7]
+[feature_fraction_bynode: 1]
+[feature_fraction_seed: 2]
+[extra_trees: 0]
+[extra_seed: 6]
+[early_stopping_round: 10]
+[first_metric_only: 0]
+[max_delta_step: 0]
+[lambda_l1: 0]
+[lambda_l2: 0]
+[linear_lambda: 0]
+[min_gain_to_split: 0]
+[drop_rate: 0.1]
+[max_drop: 50]
+[skip_drop: 0.5]
+[xgboost_dart_mode: 0]
+[uniform_drop: 0]
+[drop_seed: 4]
+[top_rate: 0.2]
+[other_rate: 0.1]
+[min_data_per_group: 100]
+[max_cat_threshold: 32]
+[cat_l2: 10]
+[cat_smooth: 10]
+[max_cat_to_onehot: 4]
+[top_k: 20]
+[monotone_constraints: ]
+[monotone_constraints_method: basic]
+[monotone_penalty: 0]
+[feature_contri: ]
+[forcedsplits_filename: ]
+[refit_decay_rate: 0.9]
+[cegb_tradeoff: 1]
+[cegb_penalty_split: 0]
+[cegb_penalty_feature_lazy: ]
+[cegb_penalty_feature_coupled: ]
+[path_smooth: 0]
+[interaction_constraints: ]
+[verbosity: 1]
+[saved_feature_importance_type: 0]
+[max_bin: 255]
+[max_bin_by_feature: ]
+[min_data_in_bin: 3]
+[bin_construct_sample_cnt: 200000]
+[data_random_seed: 1]
+[is_enable_sparse: 1]
+[enable_bundle: 1]
+[use_missing: 1]
+[zero_as_missing: 0]
+[feature_pre_filter: 1]
+[pre_partition: 0]
+[two_round: 0]
+[header: 0]
+[label_column: ]
+[weight_column: ]
+[group_column: ]
+[ignore_column: ]
+[categorical_feature: ]
+[forcedbins_filename: ]
+[objective_seed: 5]
+[num_class: 1]
+[is_unbalance: 0]
+[scale_pos_weight: 1]
+[sigmoid: 1]
+[boost_from_average: 1]
+[reg_sqrt: 0]
+[alpha: 0.9]
+[fair_c: 1]
+[poisson_max_delta_step: 0.7]
+[tweedie_variance_power: 1.5]
+[lambdarank_truncation_level: 30]
+[lambdarank_norm: 1]
+[label_gain: ]
+[eval_at: ]
+[multi_error_top_k: 1]
+[auc_mu_weights: ]
+[num_machines: 1]
+[local_listen_port: 12400]
+[time_out: 120]
+[machine_list_filename: ]
+[machines: ]
+[gpu_platform_id: -1]
+[gpu_device_id: -1]
+[gpu_use_dp: 0]
+[num_gpu: 1]
+
+end of parameters
+
+pandas_categorical:[]
diff --git a/data/faction-picker-bot/models/fakirs_model.txt b/data/faction-picker-bot/models/fakirs_model.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8c74a012b7c1528ac459cfb9be7f0f82579dd6ce
--- /dev/null
+++ b/data/faction-picker-bot/models/fakirs_model.txt
@@ -0,0 +1,961 @@
+tree
+version=v3
+num_class=1
+num_tree_per_iteration=1
+label_index=0
+max_feature_idx=73
+objective=regression
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+leaf_value=0.14771426132073004 -0.079637488813779522 0.39025190565561835 0.79795822785503567 -0.87441211403280072 0.7376225800315539 -0.10516218649781456 -0.020100458129113461 0.76215506423922152 -0.16030192531757809 -0.58133149775253101 0.034049236045466912 -1.0120250858011699 -0.02998917195837324 0.020029439904500505 0.66470324162577032 -0.53594712707890224 -0.22021225612787976 0.44499390770250297 -0.16792535184353008 0.65080925649718235 -0.38662223165685483 -0.6254731474669426 1.3860920077020473 0.055672030443343047 0.025698622720582143 1.2345133682092033 -0.00096395204706889838 0.20156464111059905 -0.84659055874993416 -0.37039260351954684
+leaf_weight=1200 908 430 87 69 60 237 119 34 596 44 278 21 600 370 61 73 21 134 204 76 22 91 22 107 140 24 41 32 96 492
+leaf_count=1200 908 430 87 69 60 237 119 34 596 44 278 21 600 370 61 73 21 134 204 76 22 91 22 107 140 24 41 32 96 492
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+internal_weight=0 4849 3191 1657 1448 379 209 271 1069 382 338 140 1534 934 504 443 108 1658 1480 1276 178 687 156 1840 1241 164 1077 169 137 599
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+is_linear=0
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+
+
+Tree=38
+num_leaves=31
+num_cat=0
+split_feature=64 68 66 53 55 40 9 50 20 10 63 28 48 60 54 57 28 43 20 27 62 17 57 51 23 16 37 28 27 60
+split_gain=5210.35 3077.19 1746.53 2075.18 2025.07 2522.39 2470.43 3208.73 2504.99 1793.32 1948.53 1954.71 1672.53 1647.06 1632.04 1532.26 2143.11 1458.73 2169.51 1785.79 1363.48 1356.95 1439.63 2768.28 1449.69 2207.02 1432.13 1353.51 1591.15 2468.77
+threshold=1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.5000000000000002 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35
+decision_type=2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
+left_child=1 2 9 12 5 15 7 8 -6 10 21 -12 14 -13 -4 -5 20 18 19 -16 -17 22 23 24 25 26 -1 28 -3 -30
+right_child=-2 27 3 4 6 -7 -8 -9 -10 -11 11 13 -14 -15 17 16 -18 -19 -20 -21 -22 -23 -24 -25 -26 -27 -28 -29 29 -31
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+leaf_weight=87 2192 1534 79 73 328 27 71 47 33 141 674 25 42 50 117 60 30 20 21 27 69 37 277 27 22 22 21 234 89 213
+leaf_count=87 2192 1534 79 73 328 27 71 47 33 141 674 25 42 50 117 60 30 20 21 27 69 37 277 27 22 22 21 234 89 213
+internal_value=0 0.0616186 0.138014 0.040377 0.131161 0.356434 0.00935361 0.10409 0.00290154 0.211718 0.17335 0.071731 -0.178573 0.556015 -0.0853231 0.249972 0.0758376 0.0771537 0.174917 0.313391 -0.10121 0.327737 0.374995 0.596028 0.761772 0.634727 0.448762 -0.027952 0.000915999 -0.208895
+internal_weight=0 4497 2427 1044 738 259 479 408 361 1383 1242 749 306 75 264 232 159 185 165 144 129 493 456 179 152 130 108 2070 1836 302
+internal_count=6689 4497 2427 1044 738 259 479 408 361 1383 1242 749 306 75 264 232 159 185 165 144 129 493 456 179 152 130 108 2070 1836 302
+is_linear=0
+shrinkage=0.1
+
+
+Tree=39
+num_leaves=31
+num_cat=0
+split_feature=70 64 14 63 73 62 20 28 68 33 51 9 57 23 73 45 28 64 67 45 23 25 8 1 0 37 33 0 71 63
+split_gain=5192.14 3401.27 2081.07 2406.83 2320.46 5265.81 2711.14 2495.64 2197.38 2466.3 2874.77 2818.29 1915.98 3092.73 2886.52 2806.96 1915.9 1885.28 1800.91 2210.66 1782.08 1753.71 1651.15 1711.31 1614.87 1555.07 2414.06 3263.09 1705.57 1610.17
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+decision_type=2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
+left_child=1 2 8 4 6 -6 7 -4 25 -10 11 24 13 14 15 16 21 18 19 20 -14 -2 23 -19 -11 26 -1 28 -28 -30
+right_child=12 -3 3 -5 5 -7 -8 -9 9 10 -12 -13 17 -15 -16 -17 -18 22 -20 -21 -22 -23 -24 -25 -26 -27 27 -29 29 -31
+leaf_value=0.15673397474844297 -0.053295958882671746 -0.030273047654287816 -0.1483633601712063 0.41707904303073884 -0.94081602493921912 0.077072877795607952 0.40570712887471727 0.33777432044347133 0.00049942335061301934 0.087186306950789466 -0.26595971618707365 0.79912035701175532 -0.024930876873133889 0.35352499711102459 0.27290952851374944 -0.51853576284997605 -0.37506457773538737 -0.12776875800590745 -0.15817788817636347 0.22133771625511789 -0.35080223757287732 0.2682621614933014 -0.60180185586214074 -0.45595734090260837 0.54826191234588628 -0.016943034777881078 0.15613391972505131 0.44162212249636651 -0.62370022847489626 -0.14618635928513957
+leaf_weight=1159 279 1684 220 25 24 27 31 30 1169 79 26 24 477 58 66 34 39 291 345 85 46 50 24 46 25 197 26 25 27 51
+leaf_count=1159 279 1684 220 25 24 27 31 30 1169 79 26 24 477 58 66 34 39 291 345 85 46 50 24 46 25 197 26 25 27 51
+internal_value=0 0.027136 0.0576816 -0.0560264 -0.0916518 -0.401934 -0.0353373 -0.0900268 0.072138 0.0252774 0.213365 0.310728 -0.0715121 0.00912981 -0.0335516 -0.0838662 -0.0437065 -0.103793 -0.0669324 -0.0151566 -0.0535926 -0.00442694 -0.201102 -0.172566 0.198022 0.113887 0.133897 -0.071282 -0.194576 -0.31148
+internal_weight=0 4849 3165 357 332 51 281 250 2808 1323 154 128 1840 526 468 402 368 1314 953 608 523 329 361 337 104 1485 1288 129 104 78
+internal_count=6689 4849 3165 357 332 51 281 250 2808 1323 154 128 1840 526 468 402 368 1314 953 608 523 329 361 337 104 1485 1288 129 104 78
+is_linear=0
+shrinkage=0.05
+
+
+end of trees
+
+feature_importances:
+black=58
+green=51
+BON4=49
+BON5=48
+no_players=46
+BON8=45
+red=43
+BON3=40
+BON6=36
+blue=35
+BON7=32
+grey=30
+BON1=28
+BON10=28
+BON2=27
+BON9=26
+brown=26
+x0_SCORE3=21
+x0_SCORE8=16
+x4_SCORE6=16
+x5_SCORE2=16
+x5_SCORE5=16
+x0_map1=16
+x2_SCORE1=15
+x3_SCORE4=15
+x3_SCORE2=14
+x0_map3=14
+x0_SCORE2=13
+x1_SCORE7=13
+x4_SCORE7=13
+x1_SCORE1=12
+x1_SCORE6=12
+x2_SCORE3=12
+x2_SCORE5=12
+x3_SCORE7=12
+x4_SCORE2=12
+x5_SCORE6=12
+x5_SCORE8=12
+x0_map2=12
+x0_SCORE1=11
+x1_SCORE8=11
+x2_SCORE8=11
+x3_SCORE1=11
+x0_SCORE4=10
+x0_SCORE9=10
+x2_SCORE6=10
+x2_SCORE7=10
+x3_SCORE6=10
+x5_SCORE4=10
+x1_SCORE9=9
+x3_SCORE5=9
+x4_SCORE3=9
+x4_SCORE5=9
+x4_SCORE9=9
+x0_SCORE7=8
+x1_SCORE3=8
+x5_SCORE7=8
+x2_SCORE2=7
+x3_SCORE8=7
+x4_SCORE8=7
+x5_SCORE3=7
+x4_SCORE4=6
+x1_SCORE2=5
+x2_SCORE4=5
+x5_SCORE9=5
+x0_SCORE5=4
+x0_SCORE6=4
+x1_SCORE5=4
+x3_SCORE3=4
+x3_SCORE9=4
+x2_SCORE9=3
+x1_SCORE4=1
+
+parameters:
+[boosting: dart]
+[objective: regression]
+[metric: l2]
+[tree_learner: serial]
+[device_type: cpu]
+[linear_tree: 0]
+[data: ]
+[valid: ]
+[num_iterations: 40]
+[learning_rate: 0.1]
+[num_leaves: 31]
+[num_threads: 0]
+[deterministic: 0]
+[force_col_wise: 0]
+[force_row_wise: 0]
+[histogram_pool_size: -1]
+[max_depth: -1]
+[min_data_in_leaf: 20]
+[min_sum_hessian_in_leaf: 0.001]
+[bagging_fraction: 1]
+[pos_bagging_fraction: 1]
+[neg_bagging_fraction: 1]
+[bagging_freq: 0]
+[bagging_seed: 3]
+[feature_fraction: 0.7]
+[feature_fraction_bynode: 1]
+[feature_fraction_seed: 2]
+[extra_trees: 0]
+[extra_seed: 6]
+[early_stopping_round: 10]
+[first_metric_only: 0]
+[max_delta_step: 0]
+[lambda_l1: 0]
+[lambda_l2: 0]
+[linear_lambda: 0]
+[min_gain_to_split: 0]
+[drop_rate: 0.1]
+[max_drop: 50]
+[skip_drop: 0.5]
+[xgboost_dart_mode: 0]
+[uniform_drop: 0]
+[drop_seed: 4]
+[top_rate: 0.2]
+[other_rate: 0.1]
+[min_data_per_group: 100]
+[max_cat_threshold: 32]
+[cat_l2: 10]
+[cat_smooth: 10]
+[max_cat_to_onehot: 4]
+[top_k: 20]
+[monotone_constraints: ]
+[monotone_constraints_method: basic]
+[monotone_penalty: 0]
+[feature_contri: ]
+[forcedsplits_filename: ]
+[refit_decay_rate: 0.9]
+[cegb_tradeoff: 1]
+[cegb_penalty_split: 0]
+[cegb_penalty_feature_lazy: ]
+[cegb_penalty_feature_coupled: ]
+[path_smooth: 0]
+[interaction_constraints: ]
+[verbosity: 1]
+[saved_feature_importance_type: 0]
+[max_bin: 255]
+[max_bin_by_feature: ]
+[min_data_in_bin: 3]
+[bin_construct_sample_cnt: 200000]
+[data_random_seed: 1]
+[is_enable_sparse: 1]
+[enable_bundle: 1]
+[use_missing: 1]
+[zero_as_missing: 0]
+[feature_pre_filter: 1]
+[pre_partition: 0]
+[two_round: 0]
+[header: 0]
+[label_column: ]
+[weight_column: ]
+[group_column: ]
+[ignore_column: ]
+[categorical_feature: ]
+[forcedbins_filename: ]
+[objective_seed: 5]
+[num_class: 1]
+[is_unbalance: 0]
+[scale_pos_weight: 1]
+[sigmoid: 1]
+[boost_from_average: 1]
+[reg_sqrt: 0]
+[alpha: 0.9]
+[fair_c: 1]
+[poisson_max_delta_step: 0.7]
+[tweedie_variance_power: 1.5]
+[lambdarank_truncation_level: 30]
+[lambdarank_norm: 1]
+[label_gain: ]
+[eval_at: ]
+[multi_error_top_k: 1]
+[auc_mu_weights: ]
+[num_machines: 1]
+[local_listen_port: 12400]
+[time_out: 120]
+[machine_list_filename: ]
+[machines: ]
+[gpu_platform_id: -1]
+[gpu_device_id: -1]
+[gpu_use_dp: 0]
+[num_gpu: 1]
+
+end of parameters
+
+pandas_categorical:[]
diff --git a/data/faction-picker-bot/models/giants_model.txt b/data/faction-picker-bot/models/giants_model.txt
new file mode 100644
index 0000000000000000000000000000000000000000..089a95f5565dd066fbebf0596674d9d6eddb49e4
--- /dev/null
+++ b/data/faction-picker-bot/models/giants_model.txt
@@ -0,0 +1,961 @@
+tree
+version=v3
+num_class=1
+num_tree_per_iteration=1
+label_index=0
+max_feature_idx=73
+objective=regression
+feature_names=x0_SCORE1 x0_SCORE2 x0_SCORE3 x0_SCORE4 x0_SCORE5 x0_SCORE6 x0_SCORE7 x0_SCORE8 x0_SCORE9 x1_SCORE1 x1_SCORE2 x1_SCORE3 x1_SCORE4 x1_SCORE5 x1_SCORE6 x1_SCORE7 x1_SCORE8 x1_SCORE9 x2_SCORE1 x2_SCORE2 x2_SCORE3 x2_SCORE4 x2_SCORE5 x2_SCORE6 x2_SCORE7 x2_SCORE8 x2_SCORE9 x3_SCORE1 x3_SCORE2 x3_SCORE3 x3_SCORE4 x3_SCORE5 x3_SCORE6 x3_SCORE7 x3_SCORE8 x3_SCORE9 x4_SCORE1 x4_SCORE2 x4_SCORE3 x4_SCORE4 x4_SCORE5 x4_SCORE6 x4_SCORE7 x4_SCORE8 x4_SCORE9 x5_SCORE2 x5_SCORE3 x5_SCORE4 x5_SCORE5 x5_SCORE6 x5_SCORE7 x5_SCORE8 x5_SCORE9 BON1 BON2 BON3 BON4 BON5 BON6 BON7 BON8 BON9 BON10 no_players red blue green black grey yellow brown x0_map1 x0_map2 x0_map3
+feature_infos=[0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] none [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:3] none [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1]
+tree_sizes=2798 2898 2878 2908 2903 2907 2880 2939 2883 2906 2913 2929 2893 2905 2900 2913 2898 2885 2893 2896 2925 2898 2892 2914 2894 2895 2884 2921 2903 2888 2908 2893 2917 2892 2932 2950 2862 2889 2864 2901
+
+Tree=0
+num_leaves=31
+num_cat=0
+split_feature=69 68 67 65 58 65 67 60 57 60 58 60 57 67 7 58 60 65 12 54 59 62 55 21 65 67 39 57 37 59
+split_gain=103156 84987.9 45602.9 34666.6 21790.4 16909.2 18354.6 14784.7 14652 12517.5 14069.4 10422.5 10238.2 9823.28 9929.04 8361.45 8087.95 9653.36 7654.21 7313.65 6747.29 6682.15 6324.49 6678.25 5850.04 5710.01 5594.72 5407.73 5328.08 5828.07
+threshold=1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35
+decision_type=2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
+left_child=1 2 3 7 13 6 -3 21 24 10 -5 20 -4 14 -2 -7 17 19 27 -14 -10 -1 -11 -24 25 28 -21 -12 29 -6
+right_child=4 5 12 9 8 15 -8 -9 11 22 18 -13 16 -15 -16 -17 -18 -19 -20 26 -22 -23 23 -25 -26 -27 -28 -29 -30 -31
+leaf_value=97.76821714295572 96.078112450434631 97.006297835882464 96.883965578770585 96.545939361755643 97.683836190256869 95.419256138796754 96.107528322724292 97.332144320674175 96.812777260919688 97.022019611920797 97.98084832777765 95.893558409577707 97.89929958222109 95.362114901220025 94.75526476017167 96.030797086028173 96.336757946697873 96.344499968984408 98.816217146876383 97.061929764841508 96.161755542154054 98.60314994536796 96.605375638727054 95.642375108310688 96.254617707341126 96.455378136315517 95.702883807317676 97.15347482264022 98.224205130840602 96.49262015386735
+leaf_weight=225 571 358 871 192 63 302 622 592 219 324 119 1512 93 526 63 861 1356 275 45 191 583 167 544 83 471 294 36 235 37 118
+leaf_count=225 571 358 871 192 63 302 622 592 219 324 119 1512 93 526 63 861 1356 275 45 191 583 167 544 83 471 294 36 235 37 118
+internal_value=96.4353 96.662 96.8752 97.1838 96.0544 96.1299 96.4359 97.6476 96.1855 96.8879 97.2493 96.0481 96.5989 95.6816 95.9467 95.872 96.4716 96.779 97.5878 97.1524 96.3395 98.1239 96.6633 96.4779 96.509 96.7429 96.8464 97.4316 97.1308 96.9072
+internal_weight=0 7491 5348 2526 4457 2143 980 984 3297 1542 591 2314 2822 1160 634 1163 1951 595 399 320 802 392 951 627 983 512 227 354 218 181
+internal_count=11948 7491 5348 2526 4457 2143 980 984 3297 1542 591 2314 2822 1160 634 1163 1951 595 399 320 802 392 951 627 983 512 227 354 218 181
+is_linear=0
+shrinkage=1
+
+
+Tree=1
+num_leaves=31
+num_cat=0
+split_feature=53 69 56 70 67 60 54 68 54 60 55 70 48 67 54 68 68 60 60 60 70 67 60 60 52 68 70 31 69 44
+split_gain=179878 52335.8 48960.4 46775.9 33062.7 29972 19791.8 19076.5 18973.6 14396.4 12121.9 12082.5 12344.5 11690 11151.9 10417.7 9791.24 9478.32 8299.35 7456.03 7345.65 6782.5 6604.69 6276.8 6172.69 5659.14 5459.51 5102.52 5382 5049.97
+threshold=1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35
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+left_child=1 24 7 13 6 -4 8 16 15 10 23 21 20 14 -3 -5 17 -2 -19 -11 28 22 -12 -9 26 -26 27 -1 29 -13
+right_child=2 3 5 4 -6 -7 -8 9 -10 19 11 12 -14 -15 -16 -17 -18 18 -20 -21 -22 -23 -24 -25 25 -27 -28 -29 -30 -31
+leaf_value=1.0062646050964084 0.48835569500359521 0.37401293445441686 0.000815692136975893 0.4593505701114392 -0.17158795954557637 -0.17326297947792807 0.23877124556675855 0.26005606989198704 -1.3914397568187931 -0.087257338774564097 0.20921886497097983 0.13052732721571961 -1.5493342088801523 0.10585502577699851 1.5802397623658182 -0.47051477120681251 0.018071578691138866 0.15935424233591833 -1.3659901740650335 -0.59300626802632184 0.59321245549246671 0.96165622211084134 -0.64129890274847501 1.0893997220889382 0.54526070844043384 -0.88381592682429722 0.42710737231107343 -0.30947825933295875 -1.0481650007076753 -0.497482538772543
+leaf_weight=35 423 177 1853 58 1078 3180 741 299 22 998 341 124 21 523 32 88 470 43 24 127 40 41 52 46 22 35 755 124 39 137
+leaf_count=35 423 177 1853 58 1078 3180 741 299 22 998 341 124 21 523 32 88 470 43 24 127 40 41 52 46 22 35 755 124 39 137
+internal_value=0 0.113573 -0.050749 0.0436606 -0.0268832 -0.109172 0.144725 0.0404277 -0.270087 -0.0259413 0.0909101 -0.0304801 -0.281595 0.23515 0.558698 -0.101116 0.197017 0.368659 -0.387038 -0.144351 -0.203293 0.178396 0.0966822 0.370635 0.309343 -0.332242 0.349355 -0.0198493 -0.309494 -0.199118
+internal_weight=0 3690 8258 2719 1987 5033 909 3225 168 2265 1140 795 361 732 209 146 960 490 67 1125 340 434 393 345 971 57 914 159 300 261
+internal_count=11948 3690 8258 2719 1987 5033 909 3225 168 2265 1140 795 361 732 209 146 960 490 67 1125 340 434 393 345 971 57 914 159 300 261
+is_linear=0
+shrinkage=0.1
+
+
+Tree=39
+num_leaves=31
+num_cat=0
+split_feature=68 69 70 55 50 18 65 70 7 52 7 69 35 5 0 65 62 65 70 57 73 0 38 34 63 70 49 70 52 18
+split_gain=6967.24 6376.15 5209.1 3304.65 2841.67 4044.16 2798.78 2927.74 3628.27 2705.76 4804.66 3060.1 4143.21 2637.69 2702.89 2444.01 5188.09 2307.73 2705.2 2509.63 2447.99 3140.62 2885.01 3379.82 2987.81 3037.18 2594.18 4057.54 2306.82 3477.03
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+decision_type=2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
+left_child=1 2 3 -1 5 -4 -3 -8 -9 11 -11 17 -13 14 15 16 -6 18 19 -2 22 -22 23 26 -24 -26 -19 -28 -5 -30
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+leaf_count=1012 151 1918 1912 1659 61 323 1458 348 40 193 22 609 26 49 48 116 52 628 464 298 135 22 46 82 45 33 42 40 94 22
+internal_value=0 0.021301 0.05639 0.103693 0.00491228 0.0250274 -0.0285546 -0.0725025 -0.194565 -0.0684394 0.102081 -0.0824272 -0.177972 -0.132993 -0.0731755 -0.00166875 0.16383 -0.0518778 0.00655242 0.0940448 -0.101595 -0.284015 -0.070329 -0.0352179 -0.294587 -0.106106 -0.0703199 -0.334813 0.0625826 -0.152979
+internal_weight=0 9112 5348 2787 2561 2235 3764 1846 388 2836 215 2621 635 326 277 229 113 1986 913 449 1073 157 916 792 124 78 710 82 1775 116
+internal_count=11948 9112 5348 2787 2561 2235 3764 1846 388 2836 215 2621 635 326 277 229 113 1986 913 449 1073 157 916 792 124 78 710 82 1775 116
+is_linear=0
+shrinkage=0.05
+
+
+end of trees
+
+feature_importances:
+BON8=72
+blue=67
+black=57
+BON4=56
+BON2=54
+BON5=52
+grey=49
+brown=46
+BON7=44
+BON3=43
+yellow=43
+BON6=40
+BON1=31
+green=29
+BON10=21
+BON9=20
+no_players=19
+x0_SCORE8=18
+x0_SCORE2=15
+x0_SCORE7=15
+x5_SCORE9=15
+x2_SCORE4=14
+x3_SCORE5=14
+x3_SCORE8=14
+x4_SCORE3=14
+x1_SCORE4=13
+x1_SCORE8=13
+x0_SCORE1=12
+x2_SCORE1=12
+x4_SCORE9=12
+x5_SCORE5=11
+x0_map1=11
+x0_SCORE4=10
+x5_SCORE3=10
+x5_SCORE7=10
+x0_map3=10
+x0_SCORE9=9
+x4_SCORE6=9
+x1_SCORE3=8
+x2_SCORE2=8
+x2_SCORE5=8
+x2_SCORE7=8
+x3_SCORE1=8
+x3_SCORE3=8
+x3_SCORE6=8
+x3_SCORE7=8
+x0_map2=8
+x1_SCORE7=7
+x2_SCORE6=7
+x2_SCORE8=7
+x2_SCORE9=7
+x4_SCORE4=7
+x4_SCORE8=7
+x5_SCORE6=7
+x5_SCORE2=6
+x5_SCORE8=6
+x0_SCORE6=5
+x1_SCORE1=5
+x1_SCORE5=5
+x1_SCORE6=5
+x1_SCORE9=5
+x3_SCORE4=5
+x0_SCORE5=4
+x2_SCORE3=4
+x3_SCORE2=4
+x4_SCORE5=4
+x5_SCORE4=4
+x1_SCORE2=3
+x3_SCORE9=3
+x4_SCORE2=3
+x0_SCORE3=2
+x4_SCORE7=2
+
+parameters:
+[boosting: dart]
+[objective: regression]
+[metric: l2]
+[tree_learner: serial]
+[device_type: cpu]
+[linear_tree: 0]
+[data: ]
+[valid: ]
+[num_iterations: 40]
+[learning_rate: 0.1]
+[num_leaves: 31]
+[num_threads: 0]
+[deterministic: 0]
+[force_col_wise: 0]
+[force_row_wise: 0]
+[histogram_pool_size: -1]
+[max_depth: -1]
+[min_data_in_leaf: 20]
+[min_sum_hessian_in_leaf: 0.001]
+[bagging_fraction: 1]
+[pos_bagging_fraction: 1]
+[neg_bagging_fraction: 1]
+[bagging_freq: 0]
+[bagging_seed: 3]
+[feature_fraction: 0.7]
+[feature_fraction_bynode: 1]
+[feature_fraction_seed: 2]
+[extra_trees: 0]
+[extra_seed: 6]
+[early_stopping_round: 10]
+[first_metric_only: 0]
+[max_delta_step: 0]
+[lambda_l1: 0]
+[lambda_l2: 0]
+[linear_lambda: 0]
+[min_gain_to_split: 0]
+[drop_rate: 0.1]
+[max_drop: 50]
+[skip_drop: 0.5]
+[xgboost_dart_mode: 0]
+[uniform_drop: 0]
+[drop_seed: 4]
+[top_rate: 0.2]
+[other_rate: 0.1]
+[min_data_per_group: 100]
+[max_cat_threshold: 32]
+[cat_l2: 10]
+[cat_smooth: 10]
+[max_cat_to_onehot: 4]
+[top_k: 20]
+[monotone_constraints: ]
+[monotone_constraints_method: basic]
+[monotone_penalty: 0]
+[feature_contri: ]
+[forcedsplits_filename: ]
+[refit_decay_rate: 0.9]
+[cegb_tradeoff: 1]
+[cegb_penalty_split: 0]
+[cegb_penalty_feature_lazy: ]
+[cegb_penalty_feature_coupled: ]
+[path_smooth: 0]
+[interaction_constraints: ]
+[verbosity: 1]
+[saved_feature_importance_type: 0]
+[max_bin: 255]
+[max_bin_by_feature: ]
+[min_data_in_bin: 3]
+[bin_construct_sample_cnt: 200000]
+[data_random_seed: 1]
+[is_enable_sparse: 1]
+[enable_bundle: 1]
+[use_missing: 1]
+[zero_as_missing: 0]
+[feature_pre_filter: 1]
+[pre_partition: 0]
+[two_round: 0]
+[header: 0]
+[label_column: ]
+[weight_column: ]
+[group_column: ]
+[ignore_column: ]
+[categorical_feature: ]
+[forcedbins_filename: ]
+[objective_seed: 5]
+[num_class: 1]
+[is_unbalance: 0]
+[scale_pos_weight: 1]
+[sigmoid: 1]
+[boost_from_average: 1]
+[reg_sqrt: 0]
+[alpha: 0.9]
+[fair_c: 1]
+[poisson_max_delta_step: 0.7]
+[tweedie_variance_power: 1.5]
+[lambdarank_truncation_level: 30]
+[lambdarank_norm: 1]
+[label_gain: ]
+[eval_at: ]
+[multi_error_top_k: 1]
+[auc_mu_weights: ]
+[num_machines: 1]
+[local_listen_port: 12400]
+[time_out: 120]
+[machine_list_filename: ]
+[machines: ]
+[gpu_platform_id: -1]
+[gpu_device_id: -1]
+[gpu_use_dp: 0]
+[num_gpu: 1]
+
+end of parameters
+
+pandas_categorical:[]
diff --git a/data/faction-picker-bot/models/halflings_model.txt b/data/faction-picker-bot/models/halflings_model.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7a7007da297bca3a341b8b7d0486cef3ba1fd02e
--- /dev/null
+++ b/data/faction-picker-bot/models/halflings_model.txt
@@ -0,0 +1,961 @@
+tree
+version=v3
+num_class=1
+num_tree_per_iteration=1
+label_index=0
+max_feature_idx=73
+objective=regression
+feature_names=x0_SCORE1 x0_SCORE2 x0_SCORE3 x0_SCORE4 x0_SCORE5 x0_SCORE6 x0_SCORE7 x0_SCORE8 x0_SCORE9 x1_SCORE1 x1_SCORE2 x1_SCORE3 x1_SCORE4 x1_SCORE5 x1_SCORE6 x1_SCORE7 x1_SCORE8 x1_SCORE9 x2_SCORE1 x2_SCORE2 x2_SCORE3 x2_SCORE4 x2_SCORE5 x2_SCORE6 x2_SCORE7 x2_SCORE8 x2_SCORE9 x3_SCORE1 x3_SCORE2 x3_SCORE3 x3_SCORE4 x3_SCORE5 x3_SCORE6 x3_SCORE7 x3_SCORE8 x3_SCORE9 x4_SCORE1 x4_SCORE2 x4_SCORE3 x4_SCORE4 x4_SCORE5 x4_SCORE6 x4_SCORE7 x4_SCORE8 x4_SCORE9 x5_SCORE2 x5_SCORE3 x5_SCORE4 x5_SCORE5 x5_SCORE6 x5_SCORE7 x5_SCORE8 x5_SCORE9 BON1 BON2 BON3 BON4 BON5 BON6 BON7 BON8 BON9 BON10 no_players red blue green black grey yellow brown x0_map1 x0_map2 x0_map3
+feature_infos=[0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] none [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:3] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] none [0:1] [0:1] [0:1]
+tree_sizes=2846 2933 2910 2933 2936 2943 2918 2943 2919 2915 2935 2954 2931 2934 2929 2881 2942 2921 2914 2945 2947 2920 2932 2916 2919 2928 2925 2961 2921 2878 2943 2901 2935 2918 2946 2985 2912 2930 2921 2929
+
+Tree=0
+num_leaves=31
+num_cat=0
+split_feature=67 64 60 60 64 57 54 57 55 59 58 59 61 60 54 59 55 9 54 14 54 43 20 62 68 66 28 59 59 54
+split_gain=249690 121935 57521.7 42822.7 36405.5 25432.6 16212.2 14723.1 14480.2 13363.5 10256.6 9977.23 9828.47 8884.65 8172.75 7017.14 6814.78 6650.58 6590.71 6534.52 6469.3 6297.56 6293.65 6246.19 8505.28 6232.52 6220.62 6190.56 5894.05 5829.18
+threshold=1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35
+decision_type=2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
+left_child=1 2 6 18 7 8 -1 23 27 25 28 16 -12 -6 15 -7 -9 -11 -3 -16 -10 -20 -18 24 -2 -5 -21 -4 -8 -15
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+internal_weight=0 11047 5806 5241 10693 3545 2261 5677 1369 3387 1576 3999 1039 5016 2176 865 1277 2231 1854 1311 953 1418 939 1678 711 1156 136 416 537 3853
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+is_linear=0
+shrinkage=1
+
+
+Tree=1
+num_leaves=31
+num_cat=0
+split_feature=69 67 60 67 56 56 56 56 60 45 60 53 55 54 62 54 53 54 55 42 55 49 66 71 56 53 66 54 55 68
+split_gain=301696 220571 68052.8 48423.1 38108.5 31164 29880.6 13046.9 10354.6 10210.3 9499.91 9240.07 8781.98 8312.92 13732.3 7334.12 7432.76 7083.06 8085.83 6847.13 6464.11 6401.25 6383.95 6319.94 8152.73 5967.01 5419.09 5411.58 5074.62 5043.04
+threshold=1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35
+decision_type=2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
+left_child=1 2 7 5 12 22 10 20 15 -10 -3 -6 -4 -9 28 -7 -17 -12 -19 -11 -1 23 -2 -5 -25 -22 -13 -14 -15 -18
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+leaf_value=1.2255943877356394 0.15954360254593339 0.38411957590889279 0.73704663601466192 -0.4964853578316577 0.25929227824544299 0.14178256849358389 -0.16422532224678635 0.7828249132282783 -0.40750226146891572 -0.72087427153439809 0.27016547561179399 0.12308157637947317 0.61277591868129089 0.97432011965776211 0.27625165171557098 0.056461579293424394 -0.58305891611261995 0.21872726242019705 -0.12192965337377511 -1.6919650471210481 1.1197420359371335 -0.34539264476660525 -0.18807710907633252 -0.4871998734839994 -0.83897226234330424 0.67349250262613025 -0.11469011424815487 0.30717744231097027 0.5796707893287657 -0.15066493503125167
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+leaf_count=196 769 467 521 675 1102 222 5774 618 1427 167 489 910 384 207 682 193 334 438 1057 40 181 319 338 403 1071 504 801 782 482 187
+internal_value=0 0.155895 0.42076 -0.395636 0.258977 -0.242316 -0.081437 0.674427 -0.369694 -0.470973 0.113593 0.108737 0.509496 0.579827 0.488322 -0.192888 -0.296945 0.0499162 -0.0221252 -0.908525 0.888002 -0.624063 0.0534047 -0.66543 -0.742796 0.791407 0.0117694 0.407821 0.698237 -0.427862
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+is_linear=0
+shrinkage=0.0666667
+
+
+Tree=2
+num_leaves=31
+num_cat=0
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+threshold=1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.5000000000000002 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35
+decision_type=2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
+left_child=4 2 29 8 5 28 9 14 -3 -7 11 -4 23 -6 15 -5 17 -17 19 -16 21 -20 -23 24 -13 26 -26 -28 -1 -2
+right_child=1 3 10 7 13 6 -8 -9 -10 -11 -12 12 -14 -15 18 16 -18 -19 20 -21 -22 22 -24 -25 25 -27 27 -29 -30 -31
+leaf_value=0.59687453146064562 0.31771322352920217 0.12948073433946908 0.21036019114786667 -0.005642321976514696 0.012212167481776316 -0.93320420371258972 -1.8803994769399819 0.091529686536933497 -0.091443053355525397 0.39846843287157707 -0.29759391221525588 0.63463749345743437 0.36506864461410155 0.30313012385567767 -0.033871823430927414 -0.66474028961119469 0.64754674589404704 0.35083242565393452 -0.55102129161836 -0.32728706671287727 -1.8510556767384214 1.4250882214307785 -0.27471015968838253 -0.20617777116712663 0.38083170258689542 -1.2144142791628838 -0.064737593387807171 -1.0571464422039498 0.23792921782507837 -0.12712660367290179
+leaf_weight=299 1180 1789 909 1884 4427 34 22 717 1865 78 466 159 222 538 1256 337 52 50 374 1782 30 20 37 1289 140 32 286 39 1247 180
+leaf_count=299 1180 1789 909 1884 4427 34 22 717 1865 78 466 159 222 538 1256 337 52 50 374 1782 30 20 37 1289 140 32 286 39 1247 180
+internal_value=0 -0.04308 0.0561931 -0.0908223 0.097862 0.257825 -0.313561 -0.150918 0.0167213 -0.00578933 -0.021615 0.0201945 -0.059575 0.0437356 -0.180776 -0.0789641 -0.39363 -0.533529 -0.248369 -0.20598 -0.527714 -0.435602 0.32171 -0.108043 0.0847849 -0.0911237 -0.013822 -0.183827 0.30735 0.258837
+internal_weight=0 15095 4902 10193 6645 1680 134 6539 3654 112 3542 3076 2167 4965 5822 2323 439 387 3499 3038 461 431 57 1945 656 497 465 325 1546 1360
+internal_count=21740 15095 4902 10193 6645 1680 134 6539 3654 112 3542 3076 2167 4965 5822 2323 439 387 3499 3038 461 431 57 1945 656 497 465 325 1546 1360
+is_linear=0
+shrinkage=0.1
+
+
+Tree=39
+num_leaves=31
+num_cat=0
+split_feature=67 69 69 64 61 64 64 57 38 33 0 10 42 22 22 51 41 47 54 68 10 11 57 55 62 38 4 30 49 3
+split_gain=9667.85 6131.59 5048.94 5046.12 5066.3 3771.51 3486.67 3236.06 2819.86 5363.5 4633.37 2762.12 3487.77 2965.11 2934.08 3494.19 4502.32 2710.17 2700.74 3843.96 5207.12 3854.4 3446.19 2808.07 3045.71 2750.12 2627.16 3437.64 2560.21 2823.1
+threshold=1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35
+decision_type=2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
+left_child=1 3 6 4 -1 7 -2 -3 17 10 -10 12 -9 -14 15 16 -13 26 -6 20 21 22 25 24 -24 -20 -5 -28 -4 -30
+right_child=2 5 28 8 18 -7 -8 11 9 -11 -12 14 13 -15 -16 -17 -18 -19 19 -21 -22 -23 23 -25 -26 -27 27 -29 29 -31
+leaf_value=0.180410282237355 0.019973512608502941 0.10477436997970985 -0.11623245704955504 0.0038085776197538629 0.12438720015467271 -0.088981115664493071 -0.045311839715224331 -0.015047132020942634 0.043597062163279583 -0.49358768854822438 -0.3824515998755631 0.10251060727072921 -0.14960197583303689 -0.65155045588811245 0.62568566628864841 0.62310327494144446 -0.64330828636884696 0.14971220300963489 0.030838336644031236 0.10843776012693934 0.31697639234555075 0.2483781349200469 0.048483690455001088 -0.43756369658818484 -0.40983793159662674 -0.45647573618952075 0.22352291821792092 -0.27926161522284532 0.089444034024808855 -0.37507449489187555
+leaf_weight=1319 3810 732 2149 2642 845 1286 4415 1256 398 70 76 129 161 36 28 25 24 465 236 545 78 65 94 141 59 33 265 39 282 37
+leaf_count=1319 3810 732 2149 2642 845 1286 4415 1256 398 70 76 129 161 36 28 25 24 465 236 545 78 65 94 141 59 33 265 39 282 37
+internal_value=0 0.0328045 -0.0338905 0.0591161 0.10364 -0.0199332 -0.0150702 0.0172042 0.0206713 -0.0850475 -0.0247145 -0.0214344 -0.0457267 -0.241329 0.149909 0.0750677 -0.0144806 0.0375318 0.0553288 0.00868266 -0.0683237 -0.116179 -0.158269 -0.276597 -0.128255 -0.0289437 0.0198251 0.159021 -0.0966119 0.0355657
+internal_weight=0 11047 10693 7370 3415 3677 8225 2391 3955 544 474 1659 1453 197 206 178 153 3411 2096 1251 706 628 563 294 153 269 2946 304 2468 319
+internal_count=21740 11047 10693 7370 3415 3677 8225 2391 3955 544 474 1659 1453 197 206 178 153 3411 2096 1251 706 628 563 294 153 269 2946 304 2468 319
+is_linear=0
+shrinkage=0.05
+
+
+end of trees
+
+feature_importances:
+BON8=69
+BON4=67
+red=67
+blue=65
+BON2=61
+BON3=53
+green=53
+BON7=44
+BON5=42
+black=41
+BON10=39
+BON1=38
+BON9=33
+no_players=29
+yellow=29
+BON6=28
+grey=23
+x5_SCORE8=16
+x1_SCORE8=15
+x0_SCORE4=14
+x5_SCORE4=14
+x5_SCORE6=14
+x0_SCORE2=13
+x5_SCORE2=13
+x0_map1=13
+x4_SCORE7=12
+x4_SCORE8=12
+x1_SCORE6=11
+x2_SCORE6=11
+x4_SCORE2=11
+x4_SCORE3=11
+x1_SCORE1=10
+x2_SCORE7=10
+x0_map2=10
+x1_SCORE9=9
+x3_SCORE4=9
+x3_SCORE7=9
+x0_SCORE1=8
+x1_SCORE3=8
+x2_SCORE5=8
+x5_SCORE3=8
+x0_SCORE8=7
+x0_SCORE9=7
+x1_SCORE7=7
+x3_SCORE2=7
+x3_SCORE3=7
+x4_SCORE4=7
+x0_SCORE5=6
+x0_SCORE6=6
+x0_SCORE7=6
+x1_SCORE2=6
+x3_SCORE1=6
+x3_SCORE6=6
+x3_SCORE9=6
+x5_SCORE5=6
+x2_SCORE3=5
+x2_SCORE4=5
+x2_SCORE9=5
+x4_SCORE5=5
+x4_SCORE6=5
+x5_SCORE9=5
+x0_map3=5
+x0_SCORE3=4
+x2_SCORE2=4
+x2_SCORE8=4
+x4_SCORE9=4
+x2_SCORE1=3
+x5_SCORE7=2
+x1_SCORE4=1
+x1_SCORE5=1
+x3_SCORE5=1
+x3_SCORE8=1
+
+parameters:
+[boosting: dart]
+[objective: regression]
+[metric: l2]
+[tree_learner: serial]
+[device_type: cpu]
+[linear_tree: 0]
+[data: ]
+[valid: ]
+[num_iterations: 40]
+[learning_rate: 0.1]
+[num_leaves: 31]
+[num_threads: 0]
+[deterministic: 0]
+[force_col_wise: 0]
+[force_row_wise: 0]
+[histogram_pool_size: -1]
+[max_depth: -1]
+[min_data_in_leaf: 20]
+[min_sum_hessian_in_leaf: 0.001]
+[bagging_fraction: 1]
+[pos_bagging_fraction: 1]
+[neg_bagging_fraction: 1]
+[bagging_freq: 0]
+[bagging_seed: 3]
+[feature_fraction: 0.7]
+[feature_fraction_bynode: 1]
+[feature_fraction_seed: 2]
+[extra_trees: 0]
+[extra_seed: 6]
+[early_stopping_round: 10]
+[first_metric_only: 0]
+[max_delta_step: 0]
+[lambda_l1: 0]
+[lambda_l2: 0]
+[linear_lambda: 0]
+[min_gain_to_split: 0]
+[drop_rate: 0.1]
+[max_drop: 50]
+[skip_drop: 0.5]
+[xgboost_dart_mode: 0]
+[uniform_drop: 0]
+[drop_seed: 4]
+[top_rate: 0.2]
+[other_rate: 0.1]
+[min_data_per_group: 100]
+[max_cat_threshold: 32]
+[cat_l2: 10]
+[cat_smooth: 10]
+[max_cat_to_onehot: 4]
+[top_k: 20]
+[monotone_constraints: ]
+[monotone_constraints_method: basic]
+[monotone_penalty: 0]
+[feature_contri: ]
+[forcedsplits_filename: ]
+[refit_decay_rate: 0.9]
+[cegb_tradeoff: 1]
+[cegb_penalty_split: 0]
+[cegb_penalty_feature_lazy: ]
+[cegb_penalty_feature_coupled: ]
+[path_smooth: 0]
+[interaction_constraints: ]
+[verbosity: 1]
+[saved_feature_importance_type: 0]
+[max_bin: 255]
+[max_bin_by_feature: ]
+[min_data_in_bin: 3]
+[bin_construct_sample_cnt: 200000]
+[data_random_seed: 1]
+[is_enable_sparse: 1]
+[enable_bundle: 1]
+[use_missing: 1]
+[zero_as_missing: 0]
+[feature_pre_filter: 1]
+[pre_partition: 0]
+[two_round: 0]
+[header: 0]
+[label_column: ]
+[weight_column: ]
+[group_column: ]
+[ignore_column: ]
+[categorical_feature: ]
+[forcedbins_filename: ]
+[objective_seed: 5]
+[num_class: 1]
+[is_unbalance: 0]
+[scale_pos_weight: 1]
+[sigmoid: 1]
+[boost_from_average: 1]
+[reg_sqrt: 0]
+[alpha: 0.9]
+[fair_c: 1]
+[poisson_max_delta_step: 0.7]
+[tweedie_variance_power: 1.5]
+[lambdarank_truncation_level: 30]
+[lambdarank_norm: 1]
+[label_gain: ]
+[eval_at: ]
+[multi_error_top_k: 1]
+[auc_mu_weights: ]
+[num_machines: 1]
+[local_listen_port: 12400]
+[time_out: 120]
+[machine_list_filename: ]
+[machines: ]
+[gpu_platform_id: -1]
+[gpu_device_id: -1]
+[gpu_use_dp: 0]
+[num_gpu: 1]
+
+end of parameters
+
+pandas_categorical:[]
diff --git a/data/faction-picker-bot/models/mermaids_model.txt b/data/faction-picker-bot/models/mermaids_model.txt
new file mode 100644
index 0000000000000000000000000000000000000000..52f0a11a4b2b4c0f014f8cd294d920a30a4e35b5
--- /dev/null
+++ b/data/faction-picker-bot/models/mermaids_model.txt
@@ -0,0 +1,961 @@
+tree
+version=v3
+num_class=1
+num_tree_per_iteration=1
+label_index=0
+max_feature_idx=73
+objective=regression
+feature_names=x0_SCORE1 x0_SCORE2 x0_SCORE3 x0_SCORE4 x0_SCORE5 x0_SCORE6 x0_SCORE7 x0_SCORE8 x0_SCORE9 x1_SCORE1 x1_SCORE2 x1_SCORE3 x1_SCORE4 x1_SCORE5 x1_SCORE6 x1_SCORE7 x1_SCORE8 x1_SCORE9 x2_SCORE1 x2_SCORE2 x2_SCORE3 x2_SCORE4 x2_SCORE5 x2_SCORE6 x2_SCORE7 x2_SCORE8 x2_SCORE9 x3_SCORE1 x3_SCORE2 x3_SCORE3 x3_SCORE4 x3_SCORE5 x3_SCORE6 x3_SCORE7 x3_SCORE8 x3_SCORE9 x4_SCORE1 x4_SCORE2 x4_SCORE3 x4_SCORE4 x4_SCORE5 x4_SCORE6 x4_SCORE7 x4_SCORE8 x4_SCORE9 x5_SCORE2 x5_SCORE3 x5_SCORE4 x5_SCORE5 x5_SCORE6 x5_SCORE7 x5_SCORE8 x5_SCORE9 BON1 BON2 BON3 BON4 BON5 BON6 BON7 BON8 BON9 BON10 no_players red blue green black grey yellow brown x0_map1 x0_map2 x0_map3
+feature_infos=[0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] none [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:3] [0:1] none [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1]
+tree_sizes=2840 2950 2925 2951 2946 2960 2928 2963 2929 2947 2953 2968 2929 2947 2932 2930 2939 2955 2924 2947 2960 2927 2913 2914 2963 2958 2928 2963 2918 2931 2970 2912 2941 2937 2987 2980 2923 2914 2906 2950
+
+Tree=0
+num_leaves=31
+num_cat=0
+split_feature=62 67 64 64 67 69 60 67 57 57 57 60 69 60 60 54 55 57 60 54 68 54 58 64 58 73 55 59 54 21
+split_gain=649118 123379 66313.6 65486.6 48614.2 32595.2 26762.8 20912 18399.3 17233.6 13951.5 13983.7 13580 12536.9 11299.9 9218.68 9107.03 10761.3 8491.43 8401.33 8387.48 7730.07 9461.31 7454.67 7400.85 7011.1 6375.58 6226.68 6066.82 8977.28
+threshold=1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35
+decision_type=2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
+left_child=3 2 5 4 14 6 20 12 15 -8 11 -4 18 -10 -1 -3 17 -7 -5 -6 -2 22 -11 -15 -19 27 -23 -13 -9 -30
+right_child=1 8 10 7 19 16 9 28 13 21 -12 25 -14 23 -16 -17 -18 24 -20 -21 -22 26 -24 -25 -26 -27 -28 -29 29 -31
+leaf_value=102.93858018371232 104.68058713806543 103.40226597053288 103.85471284735786 102.52048915590572 102.3758370987986 104.34020396286749 104.13229370501345 101.9118243219917 103.02327324489748 104.22263737910858 103.02529118514926 103.46715441791159 101.63353196954613 102.84834820616113 102.49860679285882 103.0172655647943 103.31840110013523 104.0303146730418 102.0081331182168 101.92389487959832 104.06645343078127 103.46842599881951 103.05462080607158 102.58743794351014 103.22803615964933 103.84347013700616 102.58229126512178 102.86251905341781 101.35489023169927 102.13071357654522
+leaf_weight=911 521 845 477 498 531 217 478 358 2174 159 2678 257 642 2413 1625 2356 1483 174 923 1825 388 153 123 2005 339 137 173 505 1216 170
+leaf_count=911 521 845 477 498 531 217 478 358 2174 159 2678 257 642 2413 1625 2356 1483 174 923 1825 388 153 123 2005 339 137 173 505 1216 170
+internal_value=102.82 103.162 103.447 102.111 102.353 103.725 104.018 101.8 102.922 103.683 103.158 103.417 102.015 102.827 102.657 103.119 103.461 103.75 102.188 102.026 104.418 103.33 103.713 102.73 103.5 103.185 102.998 103.066 101.545 101.45
+internal_weight=0 18055 8262 8699 4892 4208 1995 3807 9793 1086 4054 1376 2063 6592 2536 3201 2213 730 1421 2356 909 608 282 4418 513 899 326 762 1744 1386
+internal_count=26754 18055 8262 8699 4892 4208 1995 3807 9793 1086 4054 1376 2063 6592 2536 3201 2213 730 1421 2356 909 608 282 4418 513 899 326 762 1744 1386
+is_linear=0
+shrinkage=1
+
+
+Tree=1
+num_leaves=31
+num_cat=0
+split_feature=62 67 70 67 70 70 69 60 60 54 69 60 70 56 60 54 68 54 56 54 49 31 61 42 53 68 32 20 68 22
+split_gain=525786 99937.1 103539 52676.4 34970.6 27371.1 24952.1 14245.4 13642.2 12437.4 11267.7 10189.7 9065.65 8659.83 8642.51 7685.35 7434.55 7046.12 6926.95 6742.3 7788.56 6604.83 6509.82 6341.99 7411.85 6320.66 6179 7036.8 6064.67 6497.06
+threshold=1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35
+decision_type=2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
+left_child=3 2 6 4 10 11 8 18 15 -5 28 -3 25 -8 -6 -2 -13 -10 -4 -16 -21 -20 -18 -12 -25 -11 27 -14 29 -1
+right_child=1 5 7 9 14 -7 13 -9 17 12 23 16 26 -15 19 -17 22 -19 21 20 -22 -23 -24 24 -26 -27 -28 -29 -30 -31
+leaf_value=0.21918508408965381 1.2625108080639003 0.26151109386380489 0.51972752849150583 -0.37885284313200829 -0.25912491742040022 -0.10195844871780997 0.64224669929872569 0.047526625311199867 0.82224119459205602 -0.67969995352762358 -0.30461112914248284 0.00013770673674872098 -0.68526169584887064 0.35566100729790256 -0.32115023603387527 0.84704378335133212 0.019425732082637905 0.47233673479191207 0.2664299129406823 -0.69520040908837732 -0.17987335969549084 -0.22833652125862908 0.25441320234021819 -0.52421743066774473 0.47882970737407893 -0.44405664008858037 -1.2061774723362504 -1.2313301010959956 -0.16085759785739001 -0.41366829306275277
+leaf_weight=730 298 2038 450 889 686 3119 735 2512 466 1157 966 2371 884 1293 442 589 823 567 1219 995 150 133 1442 48 103 899 152 119 399 80
+leaf_count=730 298 2038 450 889 686 3119 735 2512 466 1157 966 2371 884 1293 442 589 823 567 1219 995 150 133 1442 48 103 899 152 119 399 80
+internal_value=0 0.205142 0.375903 -0.425778 -0.270881 0.0610772 0.622603 0.150133 0.794852 -0.599526 -0.0891521 0.13727 -0.660622 0.459527 -0.456847 0.986626 0.0826527 0.630183 0.293167 -0.542315 -0.62769 0.217758 0.169029 -0.241806 0.15998 -0.576663 -0.810077 -0.750049 0.0518854 0.156681
+internal_weight=0 18055 8262 8699 4599 9793 3948 4314 1920 4100 2326 6674 3211 2028 2273 887 4636 1033 1802 1587 1145 1352 2265 1117 151 2056 1155 1003 1209 810
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+is_linear=0
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+threshold=1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 2.5000000000000004 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 2.5000000000000004 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35
+decision_type=2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
+left_child=1 7 29 6 19 15 12 8 9 21 11 -7 -3 14 -10 16 17 23 -9 -5 -12 22 -1 -6 25 -15 -4 28 -28 -2
+right_child=2 3 26 4 5 10 -8 18 13 -11 20 -13 -14 24 -16 -17 -18 -19 -20 -21 -22 -23 -24 -25 -26 -27 27 -29 -30 -31
+leaf_value=0.31099711248206435 -0.12963690368449671 0.67458509560758717 -0.0060479761827752108 0.041308319434353258 0.41938500383370364 -0.26450368745767688 0.046594108235838358 0.2752891413133069 0.66958563022144402 -0.72299602472533786 -0.43918920891980334 -1.6724869596569434 0.19856391359356448 0.56218502141641291 -0.61713812487466002 -0.1676115929785563 0.1479386118610484 -0.35661739975516565 0.0066749607458344911 0.52461603102567311 -1.9681647614638011 -0.45676639422978438 -0.41050463302034723 -0.73713837994469544 0.13976299080256654 -0.92226162269001921 -0.58396344030344938 -0.317796483779205 0.22577210405026094 -0.31987147221613366
+leaf_weight=578 2703 221 7446 2209 142 376 2405 577 479 96 24 23 774 369 35 1832 1035 394 1874 143 30 73 71 27 744 21 54 646 249 1104
+leaf_count=578 2703 221 7446 2209 142 376 2405 577 479 96 24 23 774 369 35 1832 1035 394 1874 143 30 73 71 27 744 21 54 646 249 1104
+internal_value=0 0.0638533 -0.0761509 0.0123289 -0.0474805 -0.11906 0.122009 0.164817 0.259146 0.0585073 -0.458071 -0.345666 0.304293 0.358734 0.581968 -0.0742873 0.0327028 -0.179143 0.0699105 0.0706931 -1.28862 0.162419 0.232065 0.234615 0.257551 0.482253 -0.0268787 -0.19032 0.0814628 -0.184803
+internal_weight=0 14552 12202 9635 6235 3883 3400 4917 2466 818 453 399 995 1648 514 3430 1598 563 2451 2352 54 722 649 169 1134 390 8395 949 303 3807
+internal_count=26754 14552 12202 9635 6235 3883 3400 4917 2466 818 453 399 995 1648 514 3430 1598 563 2451 2352 54 722 649 169 1134 390 8395 949 303 3807
+is_linear=0
+shrinkage=0.1
+
+
+Tree=39
+num_leaves=31
+num_cat=0
+split_feature=62 57 64 64 70 69 68 33 50 28 9 21 68 69 37 73 15 51 41 5 8 69 71 68 3 25 31 47 41 49
+split_gain=14044.7 8180.5 4139.56 3692.75 3586.98 3821.04 4395.25 3274.49 3249.84 3106.08 3768.97 3867.55 3090.19 3786.08 4115.78 6372.49 3188.21 3085.76 3080.68 3469.14 2927.97 3889.43 3947.49 2802.6 3032.53 3927.01 2669.98 9114.52 2861.36 3840.04
+threshold=1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35
+decision_type=2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
+left_child=2 4 23 12 5 6 -2 -6 -7 20 11 -11 13 16 -15 -16 -3 -18 19 -12 -5 -22 -23 -1 -25 -26 28 -28 29 -4
+right_child=1 3 26 9 7 8 -8 -9 -10 10 18 -13 -14 14 15 -17 17 -19 -20 -21 21 22 -24 24 25 -27 27 -29 -30 -31
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+leaf_weight=2550 1191 1097 2497 4690 2135 1611 710 280 252 463 80 68 3584 1257 162 25 108 28 20 20 199 21 54 2060 233 49 368 60 472 410
+leaf_count=2550 1191 1097 2497 4690 2135 1611 710 280 252 463 80 68 3584 1257 162 25 108 28 20 20 199 21 54 2060 233 49 368 60 472 410
+internal_value=0 0.025146 -0.0521911 0.000869505 0.0718051 0.10232 0.152191 0.0242452 0.0514312 -0.0285718 -0.131261 -0.0740696 0.0272731 0.0679175 0.0129711 0.231828 0.132267 -0.0960805 -0.384335 -0.271039 -0.0151046 0.143768 -0.163088 -0.021764 -0.0612537 -0.215029 -0.09129 -0.208944 -0.0763874 -0.0949275
+internal_weight=0 18055 8699 11876 6179 3764 1901 2415 1863 5615 651 531 6261 2677 1444 187 1233 136 120 100 4964 274 75 4892 2342 282 3807 428 3379 2907
+internal_count=26754 18055 8699 11876 6179 3764 1901 2415 1863 5615 651 531 6261 2677 1444 187 1233 136 120 100 4964 274 75 4892 2342 282 3807 428 3379 2907
+is_linear=0
+shrinkage=0.05
+
+
+end of trees
+
+feature_importances:
+black=80
+brown=78
+red=71
+yellow=62
+BON5=61
+BON2=59
+BON8=55
+BON4=47
+grey=46
+BON6=44
+BON3=40
+BON10=36
+green=32
+BON7=28
+x3_SCORE5=24
+x1_SCORE6=19
+no_players=19
+BON1=18
+x4_SCORE2=17
+BON9=17
+x1_SCORE1=13
+x1_SCORE3=12
+x4_SCORE3=12
+x1_SCORE7=11
+x2_SCORE3=11
+x5_SCORE4=11
+x1_SCORE2=10
+x3_SCORE2=10
+x3_SCORE9=10
+x4_SCORE8=10
+x5_SCORE8=10
+x0_map3=10
+x2_SCORE6=9
+x3_SCORE6=9
+x0_map1=9
+x0_SCORE4=8
+x0_SCORE8=8
+x2_SCORE1=8
+x5_SCORE5=8
+x0_SCORE5=7
+x2_SCORE4=7
+x2_SCORE8=7
+x3_SCORE3=7
+x4_SCORE5=7
+x4_SCORE6=7
+x4_SCORE7=7
+x5_SCORE6=7
+x5_SCORE7=7
+x0_map2=7
+x2_SCORE7=6
+x3_SCORE1=6
+x5_SCORE3=6
+x0_SCORE1=5
+x0_SCORE2=5
+x0_SCORE3=5
+x0_SCORE6=5
+x1_SCORE4=5
+x3_SCORE7=5
+x5_SCORE9=5
+x2_SCORE5=4
+x2_SCORE9=4
+x4_SCORE9=4
+x5_SCORE2=4
+x2_SCORE2=3
+x3_SCORE4=3
+x3_SCORE8=3
+x0_SCORE7=2
+x1_SCORE5=2
+x1_SCORE8=2
+x1_SCORE9=2
+x0_SCORE9=1
+x4_SCORE4=1
+
+parameters:
+[boosting: dart]
+[objective: regression]
+[metric: l2]
+[tree_learner: serial]
+[device_type: cpu]
+[linear_tree: 0]
+[data: ]
+[valid: ]
+[num_iterations: 40]
+[learning_rate: 0.1]
+[num_leaves: 31]
+[num_threads: 0]
+[deterministic: 0]
+[force_col_wise: 0]
+[force_row_wise: 0]
+[histogram_pool_size: -1]
+[max_depth: -1]
+[min_data_in_leaf: 20]
+[min_sum_hessian_in_leaf: 0.001]
+[bagging_fraction: 1]
+[pos_bagging_fraction: 1]
+[neg_bagging_fraction: 1]
+[bagging_freq: 0]
+[bagging_seed: 3]
+[feature_fraction: 0.7]
+[feature_fraction_bynode: 1]
+[feature_fraction_seed: 2]
+[extra_trees: 0]
+[extra_seed: 6]
+[early_stopping_round: 10]
+[first_metric_only: 0]
+[max_delta_step: 0]
+[lambda_l1: 0]
+[lambda_l2: 0]
+[linear_lambda: 0]
+[min_gain_to_split: 0]
+[drop_rate: 0.1]
+[max_drop: 50]
+[skip_drop: 0.5]
+[xgboost_dart_mode: 0]
+[uniform_drop: 0]
+[drop_seed: 4]
+[top_rate: 0.2]
+[other_rate: 0.1]
+[min_data_per_group: 100]
+[max_cat_threshold: 32]
+[cat_l2: 10]
+[cat_smooth: 10]
+[max_cat_to_onehot: 4]
+[top_k: 20]
+[monotone_constraints: ]
+[monotone_constraints_method: basic]
+[monotone_penalty: 0]
+[feature_contri: ]
+[forcedsplits_filename: ]
+[refit_decay_rate: 0.9]
+[cegb_tradeoff: 1]
+[cegb_penalty_split: 0]
+[cegb_penalty_feature_lazy: ]
+[cegb_penalty_feature_coupled: ]
+[path_smooth: 0]
+[interaction_constraints: ]
+[verbosity: 1]
+[saved_feature_importance_type: 0]
+[max_bin: 255]
+[max_bin_by_feature: ]
+[min_data_in_bin: 3]
+[bin_construct_sample_cnt: 200000]
+[data_random_seed: 1]
+[is_enable_sparse: 1]
+[enable_bundle: 1]
+[use_missing: 1]
+[zero_as_missing: 0]
+[feature_pre_filter: 1]
+[pre_partition: 0]
+[two_round: 0]
+[header: 0]
+[label_column: ]
+[weight_column: ]
+[group_column: ]
+[ignore_column: ]
+[categorical_feature: ]
+[forcedbins_filename: ]
+[objective_seed: 5]
+[num_class: 1]
+[is_unbalance: 0]
+[scale_pos_weight: 1]
+[sigmoid: 1]
+[boost_from_average: 1]
+[reg_sqrt: 0]
+[alpha: 0.9]
+[fair_c: 1]
+[poisson_max_delta_step: 0.7]
+[tweedie_variance_power: 1.5]
+[lambdarank_truncation_level: 30]
+[lambdarank_norm: 1]
+[label_gain: ]
+[eval_at: ]
+[multi_error_top_k: 1]
+[auc_mu_weights: ]
+[num_machines: 1]
+[local_listen_port: 12400]
+[time_out: 120]
+[machine_list_filename: ]
+[machines: ]
+[gpu_platform_id: -1]
+[gpu_device_id: -1]
+[gpu_use_dp: 0]
+[num_gpu: 1]
+
+end of parameters
+
+pandas_categorical:[]
diff --git a/data/faction-picker-bot/models/nomads_model.txt b/data/faction-picker-bot/models/nomads_model.txt
new file mode 100644
index 0000000000000000000000000000000000000000..04637a4fa223371c4639732ba578fa979c1e8f82
--- /dev/null
+++ b/data/faction-picker-bot/models/nomads_model.txt
@@ -0,0 +1,961 @@
+tree
+version=v3
+num_class=1
+num_tree_per_iteration=1
+label_index=0
+max_feature_idx=73
+objective=regression
+feature_names=x0_SCORE1 x0_SCORE2 x0_SCORE3 x0_SCORE4 x0_SCORE5 x0_SCORE6 x0_SCORE7 x0_SCORE8 x0_SCORE9 x1_SCORE1 x1_SCORE2 x1_SCORE3 x1_SCORE4 x1_SCORE5 x1_SCORE6 x1_SCORE7 x1_SCORE8 x1_SCORE9 x2_SCORE1 x2_SCORE2 x2_SCORE3 x2_SCORE4 x2_SCORE5 x2_SCORE6 x2_SCORE7 x2_SCORE8 x2_SCORE9 x3_SCORE1 x3_SCORE2 x3_SCORE3 x3_SCORE4 x3_SCORE5 x3_SCORE6 x3_SCORE7 x3_SCORE8 x3_SCORE9 x4_SCORE1 x4_SCORE2 x4_SCORE3 x4_SCORE4 x4_SCORE5 x4_SCORE6 x4_SCORE7 x4_SCORE8 x4_SCORE9 x5_SCORE2 x5_SCORE3 x5_SCORE4 x5_SCORE5 x5_SCORE6 x5_SCORE7 x5_SCORE8 x5_SCORE9 BON1 BON2 BON3 BON4 BON5 BON6 BON7 BON8 BON9 BON10 no_players red blue green black grey yellow brown x0_map1 x0_map2 x0_map3
+feature_infos=[0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] none [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:3] [0:1] [0:1] [0:1] [0:1] [0:1] none [0:1] [0:1] [0:1] [0:1]
+tree_sizes=2860 2949 2943 2938 2976 2962 2950 2985 2932 2943 2944 2983 2944 2955 2961 2929 2940 2892 2938 2981 2980 2950 2956 2900 2955 2925 2894 2946 2945 2912 2950 2934 2937 2917 2959 2993 2887 2899 2938 2932
+
+Tree=0
+num_leaves=31
+num_cat=0
+split_feature=60 64 57 64 57 57 54 54 68 67 55 59 68 59 68 58 58 58 62 54 55 55 14 67 59 37 62 54 62 62
+split_gain=192439 124810 52862.1 45423.3 35679.8 30738.8 21123.4 15305.3 13988.1 14911.2 12971.6 12874.8 12369.4 11844 8495.98 8335.15 8262.2 7520.99 7384.92 7101.66 6362.23 6207.78 6109.29 5724.02 6209.75 5616.71 5536.02 5384.35 5326.73 5111.68
+threshold=1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35
+decision_type=2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
+left_child=3 2 7 5 13 8 20 -2 9 29 25 19 16 -3 15 -5 28 -8 -17 -12 23 -9 -22 24 -4 27 -13 -7 -6 -1
+right_child=1 4 6 14 12 10 17 21 -10 -11 11 26 -14 -15 -16 18 -18 -19 -20 -21 22 -23 -24 -25 -26 -27 -28 -29 -30 -31
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+leaf_weight=304 1132 698 93 709 689 208 2183 875 598 361 245 1108 1074 2114 529 1000 3566 3082 1663 747 1874 2073 234 562 220 166 1130 901 990 340
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+internal_value=98.3775 98.2086 98.4161 98.7396 97.9283 98.9098 98.2705 98.7105 99.2859 99.5137 98.776 98.6694 97.7965 98.2247 98.473 98.5314 97.8598 98.15 98.6126 98.9693 98.4831 98.5905 98.389 98.7098 99.0525 99.0461 98.5365 99.1273 97.6769 99.8021
+internal_weight=0 21459 12328 10009 9131 6108 8248 4080 1603 1005 4505 3230 6319 2812 3901 3372 5245 5265 2663 992 2983 2948 2108 875 313 1275 2238 1109 1679 644
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+is_linear=0
+shrinkage=1
+
+
+Tree=1
+num_leaves=31
+num_cat=0
+split_feature=70 60 56 60 56 56 67 67 67 54 67 54 53 54 53 53 53 66 66 29 62 1 56 54 68 8 25 68 54 2
+split_gain=464948 111940 46234.4 38830.8 33924.9 24594.3 16683.4 14162.7 14055.9 12664.3 12481.5 11501.5 10887.8 10796 10601 10464.5 9875.44 9829.29 9463.27 8117.25 7965.93 7491.29 7448.47 6965.36 6666.71 6307.96 6197.08 5798.46 5261.48 5071.39
+threshold=1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35
+decision_type=2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
+left_child=1 4 8 22 10 13 11 9 14 -6 17 19 -11 -5 -3 -8 -7 27 -18 -4 26 24 -2 -10 -16 -15 -20 -1 -17 -29
+right_child=3 2 6 5 7 16 15 -9 23 12 -12 -13 -14 25 21 28 18 -19 20 -21 -22 -23 -24 -25 -26 -27 -28 29 -30 -31
+leaf_value=1.0439052077170294 0.0040459542231678974 0.62121323695624153 0.34356900504658278 2.3934591528158804e-05 0.644850812033398 -0.44918623386806689 0.024651149752894109 0.19671256339788451 0.30213422151268754 0.5407345207400267 0.43112458797820652 0.0019053836119651141 0.16978995322993717 -0.42249145341419864 0.30133652301544522 -0.054548769610553938 -0.57048518473802101 0.48750733624627529 -1.0376784134422175 -0.20401493856941699 -0.58563320567545196 0.7130951739265583 -0.26114939602699472 0.08865784357327558 -0.083400638208167593 0.17964619296353038 -0.41575126248132055 0.48753301686539507 -0.20029163054741475 1.4857520800370436
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+threshold=1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 2.5000000000000004 1.0000000180025095e-35 1.5000000000000002 1.0000000180025095e-35 1.5000000000000002 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35
+decision_type=2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
+left_child=11 5 3 4 -3 6 -2 -8 9 12 -5 27 13 -9 -11 -12 -17 19 -19 20 22 -22 -13 -15 -18 26 -24 -1 -29 -4
+right_child=1 2 29 10 -6 -7 7 8 -10 14 15 17 -14 23 -16 16 24 18 -20 -21 21 -23 25 -25 -26 -27 -28 28 -30 -31
+leaf_value=-0.049914555498299762 0.07693503593618517 0.18801265960889024 -0.21743595120612502 -0.2180732704876879 -0.10032478331346642 0.18253634170488975 0.043157930805936918 0.24578863655819616 -0.19168789871143735 0.99979348681777358 0.005026946259997769 0.22944308967507135 0.57143723254965761 -0.2518502648567949 0.112020933853478 0.070019067008979627 -0.24516669566787425 0.27769485051408777 -0.30794311190644902 0.020998672802494001 0.75689608544510401 -0.38703842776065528 -0.83464079458605167 0.30011032724822012 -1.0873994922994272 -1.1033563479781152 0.15911912375535719 0.20812840902514212 0.66942781629611048 -0.66551869950007381
+leaf_weight=376 3904 1429 1331 2642 1226 1356 2819 340 2836 83 2239 1243 97 1064 119 128 84 203 579 4725 196 33 44 135 92 32 156 1597 196 164
+leaf_count=376 3904 1429 1331 2642 1226 1356 2819 340 2836 83 2239 1243 97 1064 119 128 84 203 579 4725 196 33 44 135 92 32 156 1597 196 164
+internal_value=0 -0.0360232 -0.0995658 -0.0677162 0.054867 0.010489 -0.00998098 -0.0552659 -0.114628 0.00427482 -0.130485 0.0848274 -0.0540686 -0.093493 0.476799 -0.0394876 -0.367343 0.0486562 -0.155917 0.0735397 0.21923 0.59205 0.161348 -0.189703 -0.685425 -0.203487 -0.0595081 0.205081 0.258555 -0.26659
+internal_weight=0 22088 9335 7840 2655 12753 11397 7493 4674 1838 5185 9380 1636 1539 202 2543 304 7211 782 6429 1704 229 1475 1199 176 232 200 2169 1793 1495
+internal_count=31468 22088 9335 7840 2655 12753 11397 7493 4674 1838 5185 9380 1636 1539 202 2543 304 7211 782 6429 1704 229 1475 1199 176 232 200 2169 1793 1495
+is_linear=0
+shrinkage=0.1
+
+
+Tree=39
+num_leaves=31
+num_cat=0
+split_feature=70 64 64 67 57 68 49 67 9 68 67 33 46 63 31 20 23 7 26 10 25 47 14 18 37 25 6 67 4 25
+split_gain=13306.9 7462.32 4283.25 3426.71 3411.54 3898.71 3725.62 5050.91 5193.05 3316.6 3620.73 3590.77 3047.45 3233.84 2987.29 3355.78 3293.69 2955.78 2931.69 3443.57 3233.49 2846.95 2770.64 3303.34 2649.88 2415.23 6516.85 2918.95 2396.88 2892.25
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+decision_type=2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
+left_child=1 4 3 -2 5 6 -1 8 17 10 11 28 13 22 15 -6 -16 -8 19 20 -14 -13 -5 -24 -9 -11 27 -27 29 -3
+right_child=2 9 -4 12 14 -7 7 24 -10 25 -12 21 18 -15 16 -17 -18 -19 -20 -21 -22 -23 23 -25 -26 26 -28 -29 -30 -31
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+leaf_weight=1977 3342 2041 2693 2006 6562 1788 90 107 23 1446 5734 297 287 107 807 802 122 21 20 44 52 49 216 39 24 82 25 90 336 239
+leaf_count=1977 3342 2041 2693 2006 6562 1788 90 107 23 1446 5734 297 287 107 807 802 122 21 20 44 52 49 216 39 24 82 25 90 336 239
+internal_value=0 0.0202682 -0.0521596 -0.0290146 0.046549 0.084288 0.128206 -0.0478421 0.16799 -0.0110558 0.00125361 0.0461431 -0.0701264 -0.0917576 0.0282097 0.017551 0.112699 0.0263023 0.0569774 0.0877945 0.141808 0.197517 -0.104469 -0.259709 -0.268617 -0.0762064 -0.240447 -0.130809 0.0261219 0.00774905
+internal_weight=0 22662 8806 6113 12323 4030 2242 265 134 10339 8696 2962 2771 2368 8293 7364 929 111 403 383 339 346 2261 255 131 1643 197 172 2616 2280
+internal_count=31468 22662 8806 6113 12323 4030 2242 265 134 10339 8696 2962 2771 2368 8293 7364 929 111 403 383 339 346 2261 255 131 1643 197 172 2616 2280
+is_linear=0
+shrinkage=0.05
+
+
+end of trees
+
+feature_importances:
+grey=65
+BON4=62
+BON8=59
+BON2=57
+BON1=52
+BON10=51
+black=51
+red=49
+BON5=46
+BON6=44
+no_players=43
+BON3=41
+BON7=40
+green=38
+brown=25
+x0_SCORE2=18
+x0_SCORE8=17
+x1_SCORE1=15
+x0_SCORE6=14
+x0_SCORE7=14
+x0_SCORE9=14
+x1_SCORE4=14
+BON9=14
+x2_SCORE8=13
+x0_map3=13
+x0_SCORE3=12
+x3_SCORE5=12
+x5_SCORE5=12
+blue=12
+x1_SCORE2=11
+x1_SCORE6=11
+x1_SCORE8=11
+x3_SCORE7=11
+x4_SCORE7=10
+x5_SCORE4=10
+x0_map2=10
+x2_SCORE1=9
+x2_SCORE3=9
+x4_SCORE3=9
+x5_SCORE3=9
+x1_SCORE3=8
+x2_SCORE9=8
+x3_SCORE1=8
+x4_SCORE5=8
+x4_SCORE8=8
+x1_SCORE7=7
+x3_SCORE4=7
+x4_SCORE2=7
+x0_SCORE1=6
+x0_SCORE4=6
+x0_SCORE5=6
+x1_SCORE5=6
+x2_SCORE4=6
+x3_SCORE6=6
+x3_SCORE9=6
+x4_SCORE9=6
+x0_map1=6
+x2_SCORE7=5
+x3_SCORE2=5
+x3_SCORE3=5
+x4_SCORE4=5
+x5_SCORE2=5
+x5_SCORE7=5
+x1_SCORE9=4
+x2_SCORE2=4
+x5_SCORE6=4
+x2_SCORE5=3
+x2_SCORE6=3
+x3_SCORE8=3
+x4_SCORE6=3
+x5_SCORE8=3
+x5_SCORE9=1
+
+parameters:
+[boosting: dart]
+[objective: regression]
+[metric: l2]
+[tree_learner: serial]
+[device_type: cpu]
+[linear_tree: 0]
+[data: ]
+[valid: ]
+[num_iterations: 40]
+[learning_rate: 0.1]
+[num_leaves: 31]
+[num_threads: 0]
+[deterministic: 0]
+[force_col_wise: 0]
+[force_row_wise: 0]
+[histogram_pool_size: -1]
+[max_depth: -1]
+[min_data_in_leaf: 20]
+[min_sum_hessian_in_leaf: 0.001]
+[bagging_fraction: 1]
+[pos_bagging_fraction: 1]
+[neg_bagging_fraction: 1]
+[bagging_freq: 0]
+[bagging_seed: 3]
+[feature_fraction: 0.7]
+[feature_fraction_bynode: 1]
+[feature_fraction_seed: 2]
+[extra_trees: 0]
+[extra_seed: 6]
+[early_stopping_round: 10]
+[first_metric_only: 0]
+[max_delta_step: 0]
+[lambda_l1: 0]
+[lambda_l2: 0]
+[linear_lambda: 0]
+[min_gain_to_split: 0]
+[drop_rate: 0.1]
+[max_drop: 50]
+[skip_drop: 0.5]
+[xgboost_dart_mode: 0]
+[uniform_drop: 0]
+[drop_seed: 4]
+[top_rate: 0.2]
+[other_rate: 0.1]
+[min_data_per_group: 100]
+[max_cat_threshold: 32]
+[cat_l2: 10]
+[cat_smooth: 10]
+[max_cat_to_onehot: 4]
+[top_k: 20]
+[monotone_constraints: ]
+[monotone_constraints_method: basic]
+[monotone_penalty: 0]
+[feature_contri: ]
+[forcedsplits_filename: ]
+[refit_decay_rate: 0.9]
+[cegb_tradeoff: 1]
+[cegb_penalty_split: 0]
+[cegb_penalty_feature_lazy: ]
+[cegb_penalty_feature_coupled: ]
+[path_smooth: 0]
+[interaction_constraints: ]
+[verbosity: 1]
+[saved_feature_importance_type: 0]
+[max_bin: 255]
+[max_bin_by_feature: ]
+[min_data_in_bin: 3]
+[bin_construct_sample_cnt: 200000]
+[data_random_seed: 1]
+[is_enable_sparse: 1]
+[enable_bundle: 1]
+[use_missing: 1]
+[zero_as_missing: 0]
+[feature_pre_filter: 1]
+[pre_partition: 0]
+[two_round: 0]
+[header: 0]
+[label_column: ]
+[weight_column: ]
+[group_column: ]
+[ignore_column: ]
+[categorical_feature: ]
+[forcedbins_filename: ]
+[objective_seed: 5]
+[num_class: 1]
+[is_unbalance: 0]
+[scale_pos_weight: 1]
+[sigmoid: 1]
+[boost_from_average: 1]
+[reg_sqrt: 0]
+[alpha: 0.9]
+[fair_c: 1]
+[poisson_max_delta_step: 0.7]
+[tweedie_variance_power: 1.5]
+[lambdarank_truncation_level: 30]
+[lambdarank_norm: 1]
+[label_gain: ]
+[eval_at: ]
+[multi_error_top_k: 1]
+[auc_mu_weights: ]
+[num_machines: 1]
+[local_listen_port: 12400]
+[time_out: 120]
+[machine_list_filename: ]
+[machines: ]
+[gpu_platform_id: -1]
+[gpu_device_id: -1]
+[gpu_use_dp: 0]
+[num_gpu: 1]
+
+end of parameters
+
+pandas_categorical:[]
diff --git a/data/faction-picker-bot/models/swarmlings_model.txt b/data/faction-picker-bot/models/swarmlings_model.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4b62ec8a43b279ecc372d3533dc8b407b97c6cb6
--- /dev/null
+++ b/data/faction-picker-bot/models/swarmlings_model.txt
@@ -0,0 +1,959 @@
+tree
+version=v3
+num_class=1
+num_tree_per_iteration=1
+label_index=0
+max_feature_idx=73
+objective=regression
+feature_names=x0_SCORE1 x0_SCORE2 x0_SCORE3 x0_SCORE4 x0_SCORE5 x0_SCORE6 x0_SCORE7 x0_SCORE8 x0_SCORE9 x1_SCORE1 x1_SCORE2 x1_SCORE3 x1_SCORE4 x1_SCORE5 x1_SCORE6 x1_SCORE7 x1_SCORE8 x1_SCORE9 x2_SCORE1 x2_SCORE2 x2_SCORE3 x2_SCORE4 x2_SCORE5 x2_SCORE6 x2_SCORE7 x2_SCORE8 x2_SCORE9 x3_SCORE1 x3_SCORE2 x3_SCORE3 x3_SCORE4 x3_SCORE5 x3_SCORE6 x3_SCORE7 x3_SCORE8 x3_SCORE9 x4_SCORE1 x4_SCORE2 x4_SCORE3 x4_SCORE4 x4_SCORE5 x4_SCORE6 x4_SCORE7 x4_SCORE8 x4_SCORE9 x5_SCORE2 x5_SCORE3 x5_SCORE4 x5_SCORE5 x5_SCORE6 x5_SCORE7 x5_SCORE8 x5_SCORE9 BON1 BON2 BON3 BON4 BON5 BON6 BON7 BON8 BON9 BON10 no_players red blue green black grey yellow brown x0_map1 x0_map2 x0_map3
+feature_infos=[0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] none [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:3] [0:1] none [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1]
+tree_sizes=2838 2926 2924 2927 2940 2939 2922 2959 2922 2941 2952 2962 2943 2949 2929 2935 2953 2927 2943 2949 2968 2944 2929 2950 2948 2921 2947 2938 2948 2950 2940 2908 2932 2906 2974 2989 2910 2938 2921 2950
+
+Tree=0
+num_leaves=31
+num_cat=0
+split_feature=64 60 67 54 68 69 57 57 55 61 67 61 61 58 62 57 54 55 55 54 54 55 55 54 61 57 60 54 55 54
+split_gain=209656 125889 76404.9 68983.1 64139.6 53909.5 47064.7 44038.9 36229 32288 31421.3 27274.6 23639 23451.8 25073.2 23049 19877.3 19496.1 17764 16977.6 16844 16649.5 16261.7 18086.8 16125.4 15181.2 14865.2 14083.9 15873.7 13149.9
+threshold=1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35
+decision_type=2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
+left_child=1 3 7 12 5 11 24 8 -2 16 19 18 -1 -4 -15 -6 -9 -8 -3 -7 -10 -14 23 -11 -5 -23 -18 -17 -29 -13
+right_child=2 4 13 6 15 10 17 9 20 22 -12 29 21 14 -16 27 26 -19 -20 -21 -22 25 -24 -25 -26 -27 -28 28 -30 -31
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+leaf_weight=292 442 312 1235 234 1086 378 592 409 362 262 1148 467 201 2054 2016 875 328 1199 500 660 979 164 1685 604 551 434 665 660 992 588
+leaf_count=292 442 312 1235 234 1086 378 592 409 362 262 1148 467 201 2054 2016 875 328 1199 500 660 979 164 1685 604 551 434 665 660 992 588
+internal_value=101.216 101.519 100.906 102 101.288 101.561 101.718 101.159 101.572 100.973 101.224 101.956 102.667 100.633 100.749 100.982 101.359 101.435 102.391 101.623 101.313 102.385 100.761 101.113 102.364 102.121 101.117 100.816 100.644 101.62
+internal_weight=0 11333 11041 3667 7666 4053 2576 5736 1783 3953 2186 1867 1091 5305 4070 3613 1402 1791 812 1038 1341 799 2551 866 785 598 993 2527 1652 1055
+internal_count=22374 11333 11041 3667 7666 4053 2576 5736 1783 3953 2186 1867 1091 5305 4070 3613 1402 1791 812 1038 1341 799 2551 866 785 598 993 2527 1652 1055
+is_linear=0
+shrinkage=1
+
+
+Tree=1
+num_leaves=31
+num_cat=0
+split_feature=56 70 67 70 67 69 69 60 60 69 54 54 60 55 61 53 54 55 53 67 61 61 55 68 53 53 53 61 1 54
+split_gain=296453 148600 114756 108049 103581 52772.5 47335.8 37374.4 25406.8 24766.1 19542.2 17630.7 16775.8 16715.1 16298.2 15747.6 15078.8 15066.5 13759.6 13664.9 13034.2 12404.8 11887 11749.9 11620 11137.9 10969.5 10801.6 10631.7 9898.48
+threshold=1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35
+decision_type=2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
+left_child=3 2 5 4 6 8 11 20 24 13 -9 -1 -13 26 18 -12 -8 -6 -10 -5 28 -17 23 29 -2 -22 -4 -26 -3 -7
+right_child=1 7 9 19 17 22 16 10 14 -11 15 12 -14 -15 -16 21 -18 -19 -20 -21 25 -23 -24 -25 27 -27 -28 -29 -30 -31
+leaf_value=1.4489943450174947 1.1277534538575194 0.24376891779310911 0.51417608370360945 0.11072818548233604 0.5085521547834212 0.91778151019871679 0.90987338289477926 -0.33903170166346486 1.0323670219702274 -0.33181276626562406 -0.43755670212480702 1.2912326990781515 0.67622191809481624 -0.18263327914209931 0.038824654514329475 -0.56522355216172793 0.36469522767778373 0.13459409931836097 0.29618048268414676 -0.21156884289839101 0.05316197928384038 -0.92946627047079322 -0.064913357399745214 -0.23614370818040123 0.9608746114428397 -0.39410265143555656 0.025371636731539013 0.32242942285200188 -0.72868070801099138 0.25280535761799128
+leaf_weight=464 330 405 288 1544 644 139 318 1344 189 2890 978 339 471 1559 475 727 775 1867 280 941 326 970 1354 148 216 1027 700 259 57 350
+leaf_count=464 330 405 288 1544 644 139 318 1344 189 2890 978 339 471 1559 475 727 775 1867 280 941 326 970 1354 148 216 1027 700 259 57 350
+internal_value=0 -0.169956 -0.00271367 0.346491 0.528769 0.281529 0.84518 -0.433032 0.548717 -0.198238 -0.546427 1.12132 0.933615 -0.0466755 0.314078 -0.650628 0.523311 0.230504 0.592853 -0.0113167 -0.18194 -0.773424 0.0468164 0.284308 0.823872 -0.286336 0.167857 0.612754 0.123791 0.441827
+internal_weight=0 15011 9177 7363 4878 3740 2367 5834 1749 5437 4019 1274 810 2547 944 2675 1093 2511 469 2485 1815 1697 1991 637 805 1353 988 475 462 489
+internal_count=22374 15011 9177 7363 4878 3740 2367 5834 1749 5437 4019 1274 810 2547 944 2675 1093 2511 469 2485 1815 1697 1991 637 805 1353 988 475 462 489
+is_linear=0
+shrinkage=0.0666667
+
+
+Tree=2
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+threshold=1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35
+decision_type=2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
+left_child=1 3 5 10 7 17 13 24 21 -4 -1 -12 -8 -7 -15 16 -9 -3 -19 -6 -16 -5 -17 -23 -2 -26 -11 -28 -29 -22
+right_child=4 2 9 8 19 6 12 15 -10 26 11 -13 -14 14 20 22 -18 18 -20 -21 29 23 -24 -25 25 -27 27 28 -30 -31
+leaf_value=0.78914735423523252 0.17288108725986362 0.59604226466284571 0.010569406321271015 0.2037471662976468 -0.19112532695254439 0.17359378861598629 0.70509425281242999 0.12421386175060922 -0.0018903467081785427 -0.040145247875802259 0.37304016866586492 -0.91486075045289228 -0.70665775040785483 0.15141062471605207 -0.025155265003974948 -0.051832591497049434 -0.44152186654794678 0.22534249487070501 -1.102873573899269 0.093676459454946528 -0.74505229406672058 0.52717932392107814 -0.30072624491667244 1.860836375753085 -0.11437018790044065 -1.5848498306491159 -0.24091484985638592 -1.5400197992460771 -0.25198989019200607 0.30381851570887702
+leaf_weight=259 1589 458 3007 1219 4754 839 193 1246 1324 885 795 37 24 295 224 1510 156 259 25 551 224 133 1041 24 172 22 942 57 74 36
+leaf_count=259 1589 458 3007 1219 4754 839 193 1246 1324 885 795 37 24 295 224 1510 156 259 25 551 224 133 1041 24 172 22 942 57 74 36
+internal_value=0 0.0831426 0.0151828 0.218345 -0.0853414 0.175252 0.0805699 -0.0148641 0.13357 -0.0678986 0.428145 0.315766 0.548956 0.0177517 -0.150094 -0.0772654 0.0612647 0.409406 0.108422 -0.161545 -0.333862 0.263912 -0.1534 0.73105 0.123483 -0.281126 -0.188406 -0.31069 -0.81243 -0.599824
+internal_weight=0 11333 7542 3791 11041 2577 1835 5736 2700 4965 1091 832 217 1618 779 3953 1402 742 284 5305 484 1376 2551 157 1783 194 1958 1073 131 260
+internal_count=22374 11333 7542 3791 11041 2577 1835 5736 2700 4965 1091 832 217 1618 779 3953 1402 742 284 5305 484 1376 2551 157 1783 194 1958 1073 131 260
+is_linear=0
+shrinkage=0.1
+
+
+Tree=39
+num_leaves=31
+num_cat=0
+split_feature=57 55 55 64 54 51 38 61 64 47 62 23 70 33 3 41 61 20 68 69 1 64 17 62 61 21 45 35 14 11
+split_gain=16476.4 9475.06 8138.65 7255.12 5297.45 5014.57 4915.05 4508.38 4158.69 3989.57 3716.91 4211.53 3541.98 3453.83 3368.36 3365.91 3295.96 4123.91 3121.4 3101.07 3582.67 3031.3 4747.08 6441.43 2971.32 4236.94 3369.76 4210.12 2932.55 3570.58
+threshold=1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35
+decision_type=2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
+left_child=2 6 16 9 5 21 7 8 19 -4 11 -7 14 -14 -5 -12 17 -1 -16 -2 -21 22 -3 -24 25 26 27 28 29 -6
+right_child=1 4 3 12 24 10 -8 -9 -10 -11 15 -13 13 -15 18 -17 -18 -19 -20 20 -22 -23 23 -25 -26 -27 -28 -29 -30 -31
+leaf_value=0.23022789689642775 0.28760425212932217 0.034331853696048902 0.065548070778079628 0.042114282370699092 0.019564668217571579 0.35387608588786956 -0.097643125264382624 0.013435923554600594 0.022087772795684125 0.24421057504733348 0.076896767576017655 -0.19788656801712223 -0.077101467214891778 -0.41572326446872165 -0.092626711217817043 -0.428715554300027 0.12035355984871765 0.76598288677632809 -0.64165250431866427 0.021047956971106709 0.59700677509109179 -0.080994547490814126 -0.0061020057958861198 -0.9400595104694367 -0.1126859656833646 -0.21632584989263071 0.1472431067969989 -0.34631409829965348 -0.2070483161145775 -0.20063457890556982
+leaf_weight=352 435 1568 2324 1414 1602 221 693 3193 567 361 211 41 812 83 157 39 1361 40 31 270 30 1617 48 30 3778 315 264 108 201 208
+leaf_count=352 435 1568 2324 1414 1602 221 693 3193 567 361 211 41 812 83 157 39 1361 40 31 270 30 1617 48 30 3778 315 264 108 201 208
+internal_value=0 -0.028757 0.06402 0.0325161 -0.0566226 -0.00954489 0.0263028 0.0454117 0.123829 0.0895694 0.135935 0.267532 -0.0288328 -0.108504 0.0156779 -0.00197875 0.157148 0.284897 -0.183158 0.202314 0.0786438 -0.0323722 0.0153935 -0.365316 -0.0840652 -0.0439876 -0.0212068 -0.0421935 -0.0258608 -0.00574
+internal_weight=0 15439 6935 5182 10251 3775 5188 4495 1302 2685 512 262 2497 895 1602 250 1753 392 188 735 300 3263 1646 78 6476 2698 2383 2119 2011 1810
+internal_count=22374 15439 6935 5182 10251 3775 5188 4495 1302 2685 512 262 2497 895 1602 250 1753 392 188 735 300 3263 1646 78 6476 2698 2383 2119 2011 1810
+is_linear=0
+shrinkage=0.05
+
+
+end of trees
+
+feature_importances:
+BON9=112
+BON2=91
+BON3=87
+BON8=77
+BON1=76
+black=72
+red=68
+BON5=65
+yellow=55
+brown=47
+BON4=41
+grey=37
+green=33
+BON6=24
+BON10=24
+no_players=18
+x1_SCORE4=15
+x1_SCORE2=11
+x2_SCORE3=11
+BON7=11
+x3_SCORE1=10
+x4_SCORE7=10
+x0_SCORE2=9
+x1_SCORE6=9
+x5_SCORE8=9
+x1_SCORE9=8
+x2_SCORE4=8
+x3_SCORE8=8
+x4_SCORE3=8
+x5_SCORE3=8
+x5_SCORE4=8
+x0_SCORE4=7
+x0_SCORE1=6
+x2_SCORE2=6
+x5_SCORE7=6
+x0_SCORE8=5
+x1_SCORE7=5
+x2_SCORE6=5
+x2_SCORE8=5
+x3_SCORE4=5
+x4_SCORE4=5
+x4_SCORE8=5
+x5_SCORE2=5
+x0_map2=5
+x0_SCORE3=4
+x0_SCORE5=4
+x0_SCORE6=4
+x4_SCORE6=4
+x2_SCORE5=3
+x2_SCORE7=3
+x2_SCORE9=3
+x3_SCORE2=3
+x3_SCORE6=3
+x4_SCORE9=3
+x1_SCORE1=2
+x1_SCORE3=2
+x1_SCORE5=2
+x1_SCORE8=2
+x3_SCORE3=2
+x3_SCORE7=2
+x3_SCORE9=2
+x4_SCORE2=2
+x4_SCORE5=2
+x0_map1=2
+x0_SCORE7=1
+x0_SCORE9=1
+x2_SCORE1=1
+x5_SCORE5=1
+x5_SCORE9=1
+x0_map3=1
+
+parameters:
+[boosting: dart]
+[objective: regression]
+[metric: l2]
+[tree_learner: serial]
+[device_type: cpu]
+[linear_tree: 0]
+[data: ]
+[valid: ]
+[num_iterations: 40]
+[learning_rate: 0.1]
+[num_leaves: 31]
+[num_threads: 0]
+[deterministic: 0]
+[force_col_wise: 0]
+[force_row_wise: 0]
+[histogram_pool_size: -1]
+[max_depth: -1]
+[min_data_in_leaf: 20]
+[min_sum_hessian_in_leaf: 0.001]
+[bagging_fraction: 1]
+[pos_bagging_fraction: 1]
+[neg_bagging_fraction: 1]
+[bagging_freq: 0]
+[bagging_seed: 3]
+[feature_fraction: 0.7]
+[feature_fraction_bynode: 1]
+[feature_fraction_seed: 2]
+[extra_trees: 0]
+[extra_seed: 6]
+[early_stopping_round: 10]
+[first_metric_only: 0]
+[max_delta_step: 0]
+[lambda_l1: 0]
+[lambda_l2: 0]
+[linear_lambda: 0]
+[min_gain_to_split: 0]
+[drop_rate: 0.1]
+[max_drop: 50]
+[skip_drop: 0.5]
+[xgboost_dart_mode: 0]
+[uniform_drop: 0]
+[drop_seed: 4]
+[top_rate: 0.2]
+[other_rate: 0.1]
+[min_data_per_group: 100]
+[max_cat_threshold: 32]
+[cat_l2: 10]
+[cat_smooth: 10]
+[max_cat_to_onehot: 4]
+[top_k: 20]
+[monotone_constraints: ]
+[monotone_constraints_method: basic]
+[monotone_penalty: 0]
+[feature_contri: ]
+[forcedsplits_filename: ]
+[refit_decay_rate: 0.9]
+[cegb_tradeoff: 1]
+[cegb_penalty_split: 0]
+[cegb_penalty_feature_lazy: ]
+[cegb_penalty_feature_coupled: ]
+[path_smooth: 0]
+[interaction_constraints: ]
+[verbosity: 1]
+[saved_feature_importance_type: 0]
+[max_bin: 255]
+[max_bin_by_feature: ]
+[min_data_in_bin: 3]
+[bin_construct_sample_cnt: 200000]
+[data_random_seed: 1]
+[is_enable_sparse: 1]
+[enable_bundle: 1]
+[use_missing: 1]
+[zero_as_missing: 0]
+[feature_pre_filter: 1]
+[pre_partition: 0]
+[two_round: 0]
+[header: 0]
+[label_column: ]
+[weight_column: ]
+[group_column: ]
+[ignore_column: ]
+[categorical_feature: ]
+[forcedbins_filename: ]
+[objective_seed: 5]
+[num_class: 1]
+[is_unbalance: 0]
+[scale_pos_weight: 1]
+[sigmoid: 1]
+[boost_from_average: 1]
+[reg_sqrt: 0]
+[alpha: 0.9]
+[fair_c: 1]
+[poisson_max_delta_step: 0.7]
+[tweedie_variance_power: 1.5]
+[lambdarank_truncation_level: 30]
+[lambdarank_norm: 1]
+[label_gain: ]
+[eval_at: ]
+[multi_error_top_k: 1]
+[auc_mu_weights: ]
+[num_machines: 1]
+[local_listen_port: 12400]
+[time_out: 120]
+[machine_list_filename: ]
+[machines: ]
+[gpu_platform_id: -1]
+[gpu_device_id: -1]
+[gpu_use_dp: 0]
+[num_gpu: 1]
+
+end of parameters
+
+pandas_categorical:[]
diff --git a/data/faction-picker-bot/models/witches_model.txt b/data/faction-picker-bot/models/witches_model.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3cbc37533c84be59ede03c633cc920bda6f9ae8c
--- /dev/null
+++ b/data/faction-picker-bot/models/witches_model.txt
@@ -0,0 +1,961 @@
+tree
+version=v3
+num_class=1
+num_tree_per_iteration=1
+label_index=0
+max_feature_idx=73
+objective=regression
+feature_names=x0_SCORE1 x0_SCORE2 x0_SCORE3 x0_SCORE4 x0_SCORE5 x0_SCORE6 x0_SCORE7 x0_SCORE8 x0_SCORE9 x1_SCORE1 x1_SCORE2 x1_SCORE3 x1_SCORE4 x1_SCORE5 x1_SCORE6 x1_SCORE7 x1_SCORE8 x1_SCORE9 x2_SCORE1 x2_SCORE2 x2_SCORE3 x2_SCORE4 x2_SCORE5 x2_SCORE6 x2_SCORE7 x2_SCORE8 x2_SCORE9 x3_SCORE1 x3_SCORE2 x3_SCORE3 x3_SCORE4 x3_SCORE5 x3_SCORE6 x3_SCORE7 x3_SCORE8 x3_SCORE9 x4_SCORE1 x4_SCORE2 x4_SCORE3 x4_SCORE4 x4_SCORE5 x4_SCORE6 x4_SCORE7 x4_SCORE8 x4_SCORE9 x5_SCORE2 x5_SCORE3 x5_SCORE4 x5_SCORE5 x5_SCORE6 x5_SCORE7 x5_SCORE8 x5_SCORE9 BON1 BON2 BON3 BON4 BON5 BON6 BON7 BON8 BON9 BON10 no_players red blue green black grey yellow brown x0_map1 x0_map2 x0_map3
+feature_infos=[0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] none [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:3] [0:1] [0:1] none [0:1] [0:1] [0:1] [0:1] [0:1] [0:1] [0:1]
+tree_sizes=2845 2934 2957 2934 2918 2932 2904 2990 2926 2925 2918 2961 2920 2936 2948 2949 2954 2937 2909 2907 2945 2912 2911 2891 2952 2904 2895 2928 2917 2912 2899 2911 2900 2901 2957 2962 2907 2893 2924 2931
+
+Tree=0
+num_leaves=31
+num_cat=0
+split_feature=68 60 57 57 64 64 64 54 57 64 69 54 60 55 59 2 57 59 69 30 59 59 10 54 59 46 59 54 61 69
+split_gain=364513 108311 60153.5 47889.5 35465.1 24301.6 22741.4 17536.8 15628.5 14288.8 15941.8 10919.7 10055.2 9735.2 9293.17 8862.04 8755.51 8294.08 7540.34 8779.03 7123.26 7068.91 7028.45 6811.07 6214.52 5796.53 5532.61 5507.88 5507.22 5472.12
+threshold=1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35
+decision_type=2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
+left_child=1 3 6 5 8 -1 11 14 -2 10 15 26 -10 -9 22 17 -6 -4 -13 -20 -8 -18 -5 -7 28 -26 29 -14 -15 -3
+right_child=4 2 9 7 16 23 20 13 12 -11 -12 18 27 24 -16 -17 21 -19 19 -21 -22 -23 -24 -25 25 -27 -28 -29 -30 -31
+leaf_value=101.80194396984261 100.16659566716574 102.39050511351425 100.95898336247099 101.758983366091 99.703427791703831 101.68398336574546 100.85672432293998 100.95472291282708 99.997048181397261 100.21913078479113 100.3010986390364 101.1329899838021 99.831380614468173 101.23115317754866 100.96867829640831 101.46190544690748 99.558983354866072 100.42508127964715 100.79244489751959 99.785983337563692 100.47770143311419 98.969945906815752 100.1473166718507 100.91411436087489 100.3730191190844 100.96749399883092 101.18982277471662 99.487847335085007 100.63924650918005 101.25193209118962
+leaf_weight=863 1202 92 512 276 360 138 664 933 1018 5494 1969 604 730 212 967 154 280 674 650 100 1958 748 30 687 1286 188 411 1294 608 78
+leaf_count=863 1202 92 512 276 360 138 664 933 1018 5494 1969 604 730 212 967 154 280 674 650 100 1958 748 30 687 1286 188 411 1294 608 78
+internal_value=100.427 100.631 100.471 100.977 99.7178 101.431 100.766 100.806 99.8613 100.318 100.482 101.026 99.7407 100.682 101.121 100.748 99.279 100.656 100.87 100.658 100.574 99.1304 101.601 101.043 100.572 100.449 101.388 99.6118 100.792 101.868
+internal_weight=0 19548 13360 6188 5632 1688 4557 4500 4244 8803 3309 1935 3042 3227 1273 1340 1388 1186 1354 750 2622 1028 306 825 2294 1474 581 2024 820 170
+internal_count=25180 19548 13360 6188 5632 1688 4557 4500 4244 8803 3309 1935 3042 3227 1273 1340 1388 1186 1354 750 2622 1028 306 825 2294 1474 581 2024 820 170
+is_linear=0
+shrinkage=1
+
+
+Tree=1
+num_leaves=31
+num_cat=0
+split_feature=68 60 56 56 54 60 55 54 67 55 70 53 21 54 70 63 30 67 3 19 61 67 53 21 32 8 13 15 52 44
+split_gain=295255 87732 60217.6 18777.6 15435.9 13590.8 13294.6 12561.7 10699 9025.54 8515.71 8335.61 8063.43 6796.73 10666.5 6794.45 7121.25 6682.8 6505.79 6441.28 6214.15 5883.37 8644.59 6014.53 6048.9 5704.99 5478.98 5399.53 5218.1 5204.9
+threshold=1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.5000000000000002 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35
+decision_type=2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
+left_child=1 3 9 11 10 -2 -6 25 13 29 21 -1 -12 14 26 17 -17 19 -15 -8 -13 22 -4 24 -24 -5 -7 -18 -19 -3
+right_child=5 2 4 7 6 8 15 -9 -10 -11 12 20 -14 18 -16 16 27 28 -20 -21 -22 -23 23 -25 -26 -27 -28 -29 -30 -31
+leaf_value=0.84028617352753443 -0.26442164034016358 0.42742014812141105 0.59842752911678443 0.47762004247418155 -0.0076514362641197037 -0.24789924308937175 -0.21812016713293236 0.19693917950725931 -0.40610484929425983 0.17263992040877865 -0.14731897976252756 0.75436380652537749 0.3925277008526567 -0.67150640804559114 -0.75276471287512969 -0.24898905962506723 -0.51222369689114233 0.038156316695176676 -1.216493567924616 -0.97620261164293409 0.37718699286355262 0.046298998366693975 0.28256209252288988 -0.64756309607672313 -0.52134438761582613 -0.10717088054504072 0.69599325015980695 -1.4462331903787951 -0.57143352850159612 -0.13295871844271567
+leaf_weight=314 1650 1091 254 1206 2224 231 451 3395 2462 3139 1129 244 138 911 238 1898 196 911 109 56 950 1244 362 42 47 79 31 32 67 79
+leaf_count=314 1650 1091 254 1206 2224 231 451 3395 2462 3139 1129 244 138 911 238 1898 196 911 109 56 950 1244 362 42 47 79 31 32 67 79
+internal_value=0 0.122535 0.0264161 0.330057 -0.0712418 -0.425304 -0.135876 0.264135 -0.491968 0.231545 0.0460289 0.534643 -0.0885196 -0.631044 -0.429694 -0.21485 -0.291278 -0.105431 -0.729745 -0.301853 0.454265 0.133496 0.287358 0.112166 0.190182 0.441668 -0.136217 -0.643313 -0.00360495 0.389583
+internal_weight=0 19548 13360 6188 9051 5632 5835 4680 3982 4309 3216 1508 1267 1520 500 3611 2126 1485 1020 507 1194 1949 705 451 409 1285 262 228 978 1170
+internal_count=25180 19548 13360 6188 9051 5632 5835 4680 3982 4309 3216 1508 1267 1520 500 3611 2126 1485 1020 507 1194 1949 705 451 409 1285 262 228 978 1170
+is_linear=0
+shrinkage=0.0666667
+
+
+Tree=2
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+decision_type=2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
+left_child=6 4 10 -4 7 20 -1 -2 -9 19 -3 12 15 -14 -11 16 27 18 -18 -7 -6 22 -22 24 -13 26 -8 -5 -29 -30
+right_child=1 2 3 11 5 9 25 8 -10 14 -12 23 13 -15 -16 -17 17 -19 -20 -21 21 -23 -24 -25 -26 -27 -28 28 29 -31
+leaf_value=0.23691284763693377 0.11070987815776655 -0.016858840858980123 -0.018962177215928758 -0.29340931007257348 0.11957513771693616 0.24895779096421466 0.12839127385980578 0.3811979617391314 1.8376610659783887 -0.22533294216545152 0.35037821253704893 -0.015215607666155593 -0.12557674552124271 0.83696638000011436 -0.81219233861956441 -0.64339170785581423 0.35525164777114071 0.45881234788719349 -0.75540016031987278 -0.14244936136423189 -0.22749463075318813 -1.3933882777392865 0.79859534770250329 -0.63562490991183695 0.976510616232242 0.015187876500967902 0.60382509790744976 -1.0458822450323018 0.029311015811418335 -1.6961076207782915
+leaf_weight=2027 2129 2993 2522 2399 2361 354 1354 196 31 1085 361 369 432 50 122 331 81 136 97 617 203 40 40 70 56 4232 194 151 124 23
+leaf_count=2027 2129 2993 2522 2399 2361 354 1354 196 31 1085 361 369 432 50 122 331 81 136 97 617 203 40 40 70 56 4232 194 151 124 23
+internal_value=0 -0.0480908 -0.105137 -0.167798 0.0329331 -0.0271651 0.107017 0.155935 0.580098 -0.157637 0.0226679 -0.254707 -0.288875 -0.0257279 -0.284651 -0.326827 -0.292027 0.0570065 -0.249991 0.000246964 0.0803116 -0.247255 -0.0585909 0.00924503 0.115459 0.0614635 0.187974 -0.332663 -0.648672 -0.240652
+internal_weight=0 17373 10195 6841 7178 4822 7807 2356 227 2178 3354 4319 3824 482 1207 3342 3011 314 178 971 2644 283 243 495 425 5780 1548 2697 298 147
+internal_count=25180 17373 10195 6841 7178 4822 7807 2356 227 2178 3354 4319 3824 482 1207 3342 3011 314 178 971 2644 283 243 495 425 5780 1548 2697 298 147
+is_linear=0
+shrinkage=0.1
+
+
+Tree=39
+num_leaves=31
+num_cat=0
+split_feature=57 68 64 63 64 68 54 18 7 63 51 35 69 1 71 62 42 10 25 52 71 6 20 54 35 21 55 69 47 51
+split_gain=12613.6 8099.44 5483.27 4527.72 3355.1 3595.14 3314.2 4010.17 3027.07 4896.26 2927.92 2901.16 4762.52 2917.56 2977.59 2856.5 3066.45 9537.09 6761.92 2990.34 3562.26 2828.3 4335.97 3961.65 2651.36 2599.26 2571.06 4753.02 3140.07 2460.13
+threshold=1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.5000000000000002 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35 1.0000000180025095e-35
+decision_type=2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
+left_child=4 3 -3 6 5 26 11 15 9 -9 -5 13 -13 -2 -15 29 19 18 -18 24 -21 23 25 -12 -17 -23 27 -1 -29 -8
+right_child=1 2 -4 10 -6 -7 7 8 -10 -11 21 12 -14 14 -16 16 17 -19 -20 20 -22 22 -24 -25 -26 -27 -28 28 -30 -31
+leaf_value=0.30840066282006984 0.046464128319924906 -0.051707265719989987 -0.18527782225686015 -0.040955784106337774 0.019192260909351097 0.012446109901264484 0.048427713421553796 0.23474870140395931 0.3739045743162584 -0.081571734329230913 0.23019085944940648 0.65064530849456792 -0.10212503071216977 -0.0028050179855945785 0.38471358265429872 -0.0085894228832007775 0.023923221780960239 -1.1711960928440095 -0.84340565359150921 -0.0025128829043085982 -0.65768475394163817 -0.17504307056894933 0.48965550377255396 0.0056470587073787657 -0.43350989042144078 -0.79482339336758567 0.08601231807822074 0.11824554365939147 -0.31570040114844838 -0.12558926093389128
+leaf_weight=416 1543 3042 1028 6824 3807 1202 1420 236 99 254 300 55 34 78 136 1082 134 25 27 41 42 87 21 569 38 21 2017 317 48 237
+leaf_count=416 1543 3042 1028 6824 3807 1202 1420 236 99 254 300 55 34 78 136 1082 134 25 27 41 42 87 21 569 38 21 2017 317 48 237
+internal_value=0 -0.0237228 -0.0854445 -0.00483934 0.0527907 0.084768 0.0300075 0.00230032 0.121728 0.0707785 -0.0292571 0.0845664 0.36307 0.070459 0.243468 -0.0207933 -0.0736782 -0.262614 -0.121529 -0.0444663 -0.334046 0.0507346 -0.167731 0.0831651 -0.0230064 -0.295556 0.115837 0.192862 0.0611787 0.0235381
+internal_weight=0 17373 4070 13303 7807 4000 5481 3635 589 490 7822 1846 89 1757 214 3046 1389 186 161 1203 83 998 129 869 1120 108 2798 781 365 1657
+internal_count=25180 17373 4070 13303 7807 4000 5481 3635 589 490 7822 1846 89 1757 214 3046 1389 186 161 1203 83 998 129 869 1120 108 2798 781 365 1657
+is_linear=0
+shrinkage=0.05
+
+
+end of trees
+
+feature_importances:
+yellow=63
+brown=63
+black=59
+red=57
+BON4=53
+BON8=51
+BON5=44
+grey=44
+BON2=43
+BON7=42
+no_players=41
+BON3=40
+BON1=31
+x5_SCORE7=25
+BON10=25
+blue=23
+x0_SCORE2=19
+BON9=18
+x3_SCORE1=16
+x3_SCORE4=16
+x4_SCORE9=16
+x5_SCORE9=16
+x1_SCORE2=15
+x0_map3=15
+x0_SCORE4=14
+x0_SCORE7=13
+x1_SCORE8=12
+x3_SCORE3=12
+x1_SCORE5=11
+x4_SCORE3=11
+x4_SCORE5=11
+x5_SCORE4=11
+x0_map2=11
+x0_SCORE6=10
+x4_SCORE4=10
+x4_SCORE7=10
+x0_SCORE3=9
+x0_SCORE8=9
+x2_SCORE1=9
+x2_SCORE3=9
+x3_SCORE6=9
+x4_SCORE8=9
+x5_SCORE3=9
+x5_SCORE5=9
+x5_SCORE8=9
+x0_map1=9
+x0_SCORE1=8
+x0_SCORE5=8
+x3_SCORE2=8
+x4_SCORE6=8
+x5_SCORE6=8
+x2_SCORE2=7
+BON6=7
+x1_SCORE1=6
+x1_SCORE3=6
+x2_SCORE4=6
+x2_SCORE6=6
+x2_SCORE7=6
+x2_SCORE8=6
+x3_SCORE5=6
+x3_SCORE8=6
+x5_SCORE2=6
+x1_SCORE4=5
+x1_SCORE7=5
+x3_SCORE7=5
+x1_SCORE6=4
+x3_SCORE9=4
+x1_SCORE9=2
+x2_SCORE9=2
+x4_SCORE2=2
+x0_SCORE9=1
+x2_SCORE5=1
+
+parameters:
+[boosting: dart]
+[objective: regression]
+[metric: l2]
+[tree_learner: serial]
+[device_type: cpu]
+[linear_tree: 0]
+[data: ]
+[valid: ]
+[num_iterations: 40]
+[learning_rate: 0.1]
+[num_leaves: 31]
+[num_threads: 0]
+[deterministic: 0]
+[force_col_wise: 0]
+[force_row_wise: 0]
+[histogram_pool_size: -1]
+[max_depth: -1]
+[min_data_in_leaf: 20]
+[min_sum_hessian_in_leaf: 0.001]
+[bagging_fraction: 1]
+[pos_bagging_fraction: 1]
+[neg_bagging_fraction: 1]
+[bagging_freq: 0]
+[bagging_seed: 3]
+[feature_fraction: 0.7]
+[feature_fraction_bynode: 1]
+[feature_fraction_seed: 2]
+[extra_trees: 0]
+[extra_seed: 6]
+[early_stopping_round: 10]
+[first_metric_only: 0]
+[max_delta_step: 0]
+[lambda_l1: 0]
+[lambda_l2: 0]
+[linear_lambda: 0]
+[min_gain_to_split: 0]
+[drop_rate: 0.1]
+[max_drop: 50]
+[skip_drop: 0.5]
+[xgboost_dart_mode: 0]
+[uniform_drop: 0]
+[drop_seed: 4]
+[top_rate: 0.2]
+[other_rate: 0.1]
+[min_data_per_group: 100]
+[max_cat_threshold: 32]
+[cat_l2: 10]
+[cat_smooth: 10]
+[max_cat_to_onehot: 4]
+[top_k: 20]
+[monotone_constraints: ]
+[monotone_constraints_method: basic]
+[monotone_penalty: 0]
+[feature_contri: ]
+[forcedsplits_filename: ]
+[refit_decay_rate: 0.9]
+[cegb_tradeoff: 1]
+[cegb_penalty_split: 0]
+[cegb_penalty_feature_lazy: ]
+[cegb_penalty_feature_coupled: ]
+[path_smooth: 0]
+[interaction_constraints: ]
+[verbosity: 1]
+[saved_feature_importance_type: 0]
+[max_bin: 255]
+[max_bin_by_feature: ]
+[min_data_in_bin: 3]
+[bin_construct_sample_cnt: 200000]
+[data_random_seed: 1]
+[is_enable_sparse: 1]
+[enable_bundle: 1]
+[use_missing: 1]
+[zero_as_missing: 0]
+[feature_pre_filter: 1]
+[pre_partition: 0]
+[two_round: 0]
+[header: 0]
+[label_column: ]
+[weight_column: ]
+[group_column: ]
+[ignore_column: ]
+[categorical_feature: ]
+[forcedbins_filename: ]
+[objective_seed: 5]
+[num_class: 1]
+[is_unbalance: 0]
+[scale_pos_weight: 1]
+[sigmoid: 1]
+[boost_from_average: 1]
+[reg_sqrt: 0]
+[alpha: 0.9]
+[fair_c: 1]
+[poisson_max_delta_step: 0.7]
+[tweedie_variance_power: 1.5]
+[lambdarank_truncation_level: 30]
+[lambdarank_norm: 1]
+[label_gain: ]
+[eval_at: ]
+[multi_error_top_k: 1]
+[auc_mu_weights: ]
+[num_machines: 1]
+[local_listen_port: 12400]
+[time_out: 120]
+[machine_list_filename: ]
+[machines: ]
+[gpu_platform_id: -1]
+[gpu_device_id: -1]
+[gpu_use_dp: 0]
+[num_gpu: 1]
+
+end of parameters
+
+pandas_categorical:[]
diff --git a/faction-picker-bot/.ipynb_checkpoints/creatingVPdata-checkpoint.ipynb b/faction-picker-bot/.ipynb_checkpoints/creatingVPdata-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..28779b13a9d6b7acd8ee5a0b030482d18bc31f40
--- /dev/null
+++ b/faction-picker-bot/.ipynb_checkpoints/creatingVPdata-checkpoint.ipynb
@@ -0,0 +1,4093 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import math\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Table of contents\n",
+    "1. [Loading data from the website](#websiteData)\n",
+    "2. [Loading data locally](#localData)\n",
+    "3. [Dataset creation initial functions](#datasetCreate)  \n",
+    "    a. [Victory Point (target) dataset](#VPData)  \n",
+    "    b. [Features dataset](#featuresData)\n",
+    "4. [Speeding check & speed up dataset creation & sense-checking on small batch](#speedUp)\n",
+    "5. [Cleaning & filtering the data](#cleanUp)\n",
+    "6. [Creating the full dataset](#fullDataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading data from the website (not using this right now - for now we'll just load locally) <a name=\"websiteData\"></a>\n",
+    "But we'll keep this section here for now, commented out. The reason we're not using this is because the jsons are nested, which would require a bit of time to unpack. I'd like to focus on the cleaning / making some form of useful data and modelling, for now. Later we can go back and get more data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# bigoldlist = ['2013-02.json',\n",
+    "# '2013-03.json',\n",
+    "# '2013-04.json',\n",
+    "# '2013-05.json',\n",
+    "# '2013-06.json',\n",
+    "# '2013-07.json',\n",
+    "# '2013-08.json',\n",
+    "# '2013-09.json',\n",
+    "# '2013-10.json',\n",
+    "# '2013-11.json',\n",
+    "# '2013-12.json',\n",
+    "# '2014-01.json',\n",
+    "# '2014-02.json',\n",
+    "# '2014-03.json',\n",
+    "# '2014-04.json',\n",
+    "# '2014-05.json',\n",
+    "# '2014-06.json',\n",
+    "# '2014-07.json',\n",
+    "# '2014-08.json',\n",
+    "# '2014-09.json',\n",
+    "# '2014-10.json',\n",
+    "# '2014-11.json',\n",
+    "# '2014-12.json',\n",
+    "# '2015-01.json',\n",
+    "# '2015-02.json',\n",
+    "# '2015-03.json',\n",
+    "# '2015-04.json',\n",
+    "# '2015-05.json',\n",
+    "# '2015-06.json',\n",
+    "# '2015-07.json',\n",
+    "# '2015-08.json',\n",
+    "# '2015-09.json',\n",
+    "# '2015-10.json',\n",
+    "# '2015-11.json',\n",
+    "# '2015-12.json',\n",
+    "# '2016-01.json',\n",
+    "# '2016-02.json',\n",
+    "# '2016-03.json',\n",
+    "# '2016-04.json',\n",
+    "# '2016-05.json',\n",
+    "# '2016-06.json',\n",
+    "# '2016-07.json',\n",
+    "# '2016-08.json',\n",
+    "# '2016-09.json',\n",
+    "# '2016-10.json',\n",
+    "# '2016-11.json',\n",
+    "# '2016-12.json',\n",
+    "# '2017-01.json',\n",
+    "# '2017-02.json',\n",
+    "# '2017-03.json',\n",
+    "# '2017-04.json',\n",
+    "# '2017-05.json',\n",
+    "# '2017-06.json',\n",
+    "# '2017-07.json',\n",
+    "# '2017-08.json',\n",
+    "# '2017-09.json',\n",
+    "# '2017-10.json',\n",
+    "# '2017-11.json',\n",
+    "# '2017-12.json',\n",
+    "# '2018-01.json',\n",
+    "# '2018-02.json',\n",
+    "# '2018-03.json',\n",
+    "# '2018-04.json',\n",
+    "# '2018-05.json',\n",
+    "# '2018-06.json',\n",
+    "# '2018-07.json',\n",
+    "# '2018-08.json',\n",
+    "# '2018-09.json',\n",
+    "# '2018-10.json',\n",
+    "# '2018-11.json',\n",
+    "# '2018-12.json',\n",
+    "# '2019-01.json',\n",
+    "# '2019-02.json',\n",
+    "# '2019-03.json',\n",
+    "# '2019-04.json',\n",
+    "# '2019-05.json',\n",
+    "# '2019-06.json',\n",
+    "# '2019-07.json',\n",
+    "# '2019-08.json',\n",
+    "# '2019-09.json',\n",
+    "# '2019-10.json',\n",
+    "# '2019-11.json',\n",
+    "# '2019-12.json',\n",
+    "# '2020-01.json',\n",
+    "# '2020-02.json',\n",
+    "# '2020-03.json',\n",
+    "# '2020-04.json',\n",
+    "# '2020-05.json',\n",
+    "# '2020-06.json',\n",
+    "# '2020-07.json',\n",
+    "# '2020-08.json',\n",
+    "# '2020-09.json',\n",
+    "# '2020-10.json',\n",
+    "# '2020-11.json',\n",
+    "# '2020-12.json',\n",
+    "# '2021-01.json',\n",
+    "# '2021-02.json',\n",
+    "# '2021-03.json',\n",
+    "# '2021-04.json']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # Test start of web pipeline (not used)\n",
+    "# url = 'https://terra.snellman.net/data/events/' + bigoldlist[0]\n",
+    "# eventsdata1 = pd.read_json(url)\n",
+    "# print(len(eventsdata1))\n",
+    "# eventsdata1.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # Load in the data remotely\n",
+    "# for gameset in bigoldlist:\n",
+    "#     url = 'https://terra.snellman.net/data/events/' + gameset\n",
+    "#     eventsdata = pd.read_json(url)\n",
+    "    \n",
+    "#     for eachgame in range(len(gameset)):\n",
+    "#         singlerow = eventsdata.iloc[[eachgame]]\n",
+    "#         newcol = get_vp_from_game(gameEventRow)\n",
+    "#         vpdf = vpdf.append(newdf, ignore_index=True)      "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading in the data Locally <a name=\"localData\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "folderlocation = \"D:/PycharmProjects/TerraBot/terra-mystica\"\n",
+    "gameevents = pd.read_csv(f'{folderlocation}/game_events.csv')\n",
+    "games = pd.read_csv(f'{folderlocation}/games.csv')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## EDA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of game events: 73419074\n",
+      "Number of games in gameevents: 115612\n",
+      "len of games: 115612\n"
+     ]
+    }
+   ],
+   "source": [
+    "# get some more stats\n",
+    "gameslist = list(pd.unique(gameevents['game']))\n",
+    "print(f'Number of game events: {len(gameevents)}')\n",
+    "print(f\"Number of games in gameevents: {len(gameslist)}\")\n",
+    "print(f'len of games: {len(games)}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>base_map</th>\n",
+       "      <th>game</th>\n",
+       "      <th>last_update</th>\n",
+       "      <th>player_count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>126fe960806d587c78546b30f1a90853b1ada468</td>\n",
+       "      <td>0000000001</td>\n",
+       "      <td>2015-07-22 05:15:51</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>be8f6ebf549404d015547152d5f2a1906ae8dd90</td>\n",
+       "      <td>050615</td>\n",
+       "      <td>2015-07-13 09:45:00</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>126fe960806d587c78546b30f1a90853b1ada468</td>\n",
+       "      <td>0512</td>\n",
+       "      <td>2015-07-03 04:17:31</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>126fe960806d587c78546b30f1a90853b1ada468</td>\n",
+       "      <td>051501</td>\n",
+       "      <td>2015-07-16 21:00:54</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>95a66999127893f5925a5f591d54f8bcb9a670e6</td>\n",
+       "      <td>060303</td>\n",
+       "      <td>2015-07-02 05:20:19</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115607</th>\n",
+       "      <td>126fe960806d587c78546b30f1a90853b1ada468</td>\n",
+       "      <td>Youreacrookcaptianhook</td>\n",
+       "      <td>2014-09-04 05:04:45</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115608</th>\n",
+       "      <td>126fe960806d587c78546b30f1a90853b1ada468</td>\n",
+       "      <td>YourMomIsAChaosMagician</td>\n",
+       "      <td>2014-09-02 00:23:43</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115609</th>\n",
+       "      <td>126fe960806d587c78546b30f1a90853b1ada468</td>\n",
+       "      <td>ytuwertqwtr</td>\n",
+       "      <td>2014-09-28 18:12:35</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115610</th>\n",
+       "      <td>126fe960806d587c78546b30f1a90853b1ada468</td>\n",
+       "      <td>yuertyqert</td>\n",
+       "      <td>2014-09-27 19:01:17</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115611</th>\n",
+       "      <td>126fe960806d587c78546b30f1a90853b1ada468</td>\n",
+       "      <td>ZeicheMasZeuchnis</td>\n",
+       "      <td>2014-09-10 20:35:32</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>115612 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                        base_map                     game  \\\n",
+       "0       126fe960806d587c78546b30f1a90853b1ada468               0000000001   \n",
+       "1       be8f6ebf549404d015547152d5f2a1906ae8dd90                   050615   \n",
+       "2       126fe960806d587c78546b30f1a90853b1ada468                     0512   \n",
+       "3       126fe960806d587c78546b30f1a90853b1ada468                   051501   \n",
+       "4       95a66999127893f5925a5f591d54f8bcb9a670e6                   060303   \n",
+       "...                                          ...                      ...   \n",
+       "115607  126fe960806d587c78546b30f1a90853b1ada468   Youreacrookcaptianhook   \n",
+       "115608  126fe960806d587c78546b30f1a90853b1ada468  YourMomIsAChaosMagician   \n",
+       "115609  126fe960806d587c78546b30f1a90853b1ada468              ytuwertqwtr   \n",
+       "115610  126fe960806d587c78546b30f1a90853b1ada468               yuertyqert   \n",
+       "115611  126fe960806d587c78546b30f1a90853b1ada468        ZeicheMasZeuchnis   \n",
+       "\n",
+       "                last_update  player_count  \n",
+       "0       2015-07-22 05:15:51             2  \n",
+       "1       2015-07-13 09:45:00             4  \n",
+       "2       2015-07-03 04:17:31             4  \n",
+       "3       2015-07-16 21:00:54             5  \n",
+       "4       2015-07-02 05:20:19             4  \n",
+       "...                     ...           ...  \n",
+       "115607  2014-09-04 05:04:45             3  \n",
+       "115608  2014-09-02 00:23:43             4  \n",
+       "115609  2014-09-28 18:12:35             2  \n",
+       "115610  2014-09-27 19:01:17             2  \n",
+       "115611  2014-09-10 20:35:32             3  \n",
+       "\n",
+       "[115612 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "games"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>event</th>\n",
+       "      <th>faction</th>\n",
+       "      <th>game</th>\n",
+       "      <th>num</th>\n",
+       "      <th>round</th>\n",
+       "      <th>turn</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>upgrade:SH</td>\n",
+       "      <td>halflings</td>\n",
+       "      <td>0000000001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>order:2</td>\n",
+       "      <td>halflings</td>\n",
+       "      <td>0000000001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>order:2</td>\n",
+       "      <td>halflings</td>\n",
+       "      <td>0000000001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>order:1</td>\n",
+       "      <td>halflings</td>\n",
+       "      <td>0000000001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>order:1</td>\n",
+       "      <td>halflings</td>\n",
+       "      <td>0000000001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73419069</th>\n",
+       "      <td>burn</td>\n",
+       "      <td>darklings</td>\n",
+       "      <td>ZeicheMasZeuchnis</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73419070</th>\n",
+       "      <td>burn</td>\n",
+       "      <td>darklings</td>\n",
+       "      <td>ZeicheMasZeuchnis</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73419071</th>\n",
+       "      <td>favor:FAV10</td>\n",
+       "      <td>darklings</td>\n",
+       "      <td>ZeicheMasZeuchnis</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73419072</th>\n",
+       "      <td>town:TW3</td>\n",
+       "      <td>darklings</td>\n",
+       "      <td>ZeicheMasZeuchnis</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73419073</th>\n",
+       "      <td>action:ACT3</td>\n",
+       "      <td>darklings</td>\n",
+       "      <td>ZeicheMasZeuchnis</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>73419074 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                event    faction               game  num  round  turn\n",
+       "0          upgrade:SH  halflings         0000000001    1      2     1\n",
+       "1             order:2  halflings         0000000001    1      6     1\n",
+       "2             order:2  halflings         0000000001    1      2     1\n",
+       "3             order:1  halflings         0000000001    1      1     1\n",
+       "4             order:1  halflings         0000000001    1      5     1\n",
+       "...               ...        ...                ...  ...    ...   ...\n",
+       "73419069         burn  darklings  ZeicheMasZeuchnis    4      1     1\n",
+       "73419070         burn  darklings  ZeicheMasZeuchnis    3      5     2\n",
+       "73419071  favor:FAV10  darklings  ZeicheMasZeuchnis    1      3     3\n",
+       "73419072     town:TW3  darklings  ZeicheMasZeuchnis    1      6     2\n",
+       "73419073  action:ACT3  darklings  ZeicheMasZeuchnis    1      1     1\n",
+       "\n",
+       "[73419074 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gameevents"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['halflings', 'all', 'mermaids', 'dragonlords', 'riverwalkers',\n",
+       "       'yetis', 'darklings', 'chaosmagicians', 'engineers', 'icemaidens',\n",
+       "       'nomads', 'auren', 'fakirs', 'witches', 'cultists', 'alchemists',\n",
+       "       'swarmlings', 'dwarves', 'shapeshifters', 'nofaction1',\n",
+       "       'nofaction4', 'acolytes', 'giants', 'nofaction3', 'nofaction2',\n",
+       "       'nofaction5', 'nofaction7'], dtype=object)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "allfactions = pd.unique(gameevents['faction'])\n",
+    "allfactions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gamescoringtiles = pd.read_csv(f'{folderlocation}/game_scoring_tiles.csv')\n",
+    "gameoptions = pd.read_csv(f'{folderlocation}/game_options.csv')\n",
+    "stats = pd.read_csv(f'{folderlocation}/stats.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>game</th>\n",
+       "      <th>round</th>\n",
+       "      <th>tile</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0000000001</td>\n",
+       "      <td>3</td>\n",
+       "      <td>SCORE1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0000000001</td>\n",
+       "      <td>6</td>\n",
+       "      <td>SCORE5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0000000001</td>\n",
+       "      <td>5</td>\n",
+       "      <td>SCORE4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0000000001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>SCORE8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0000000001</td>\n",
+       "      <td>2</td>\n",
+       "      <td>SCORE3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>693655</th>\n",
+       "      <td>ZeicheMasZeuchnis</td>\n",
+       "      <td>4</td>\n",
+       "      <td>SCORE1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>693656</th>\n",
+       "      <td>ZeicheMasZeuchnis</td>\n",
+       "      <td>6</td>\n",
+       "      <td>SCORE5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>693657</th>\n",
+       "      <td>ZeicheMasZeuchnis</td>\n",
+       "      <td>5</td>\n",
+       "      <td>SCORE7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>693658</th>\n",
+       "      <td>ZeicheMasZeuchnis</td>\n",
+       "      <td>1</td>\n",
+       "      <td>SCORE3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>693659</th>\n",
+       "      <td>ZeicheMasZeuchnis</td>\n",
+       "      <td>2</td>\n",
+       "      <td>SCORE8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>693660 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     game  round    tile\n",
+       "0              0000000001      3  SCORE1\n",
+       "1              0000000001      6  SCORE5\n",
+       "2              0000000001      5  SCORE4\n",
+       "3              0000000001      1  SCORE8\n",
+       "4              0000000001      2  SCORE3\n",
+       "...                   ...    ...     ...\n",
+       "693655  ZeicheMasZeuchnis      4  SCORE1\n",
+       "693656  ZeicheMasZeuchnis      6  SCORE5\n",
+       "693657  ZeicheMasZeuchnis      5  SCORE7\n",
+       "693658  ZeicheMasZeuchnis      1  SCORE3\n",
+       "693659  ZeicheMasZeuchnis      2  SCORE8\n",
+       "\n",
+       "[693660 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gamescoringtiles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SCORE3    83727\n",
+       "SCORE7    83435\n",
+       "SCORE8    83322\n",
+       "SCORE4    83270\n",
+       "SCORE2    83226\n",
+       "SCORE5    83188\n",
+       "SCORE6    82770\n",
+       "SCORE1    71728\n",
+       "SCORE9    38994\n",
+       "Name: tile, dtype: int64"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gamescoringtiles['tile'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "19\n",
+      "option-errata-cultist-power                 0.106882\n",
+      "option-strict-leech                         0.106427\n",
+      "option-strict-darkling-sh                   0.103665\n",
+      "option-strict-chaosmagician-sh              0.103642\n",
+      "option-email-notify                         0.100557\n",
+      "option-mini-expansion-1                     0.089982\n",
+      "option-shipping-bonus                       0.083785\n",
+      "option-variable-turn-order                  0.075660\n",
+      "option-temple-scoring-tile                  0.053650\n",
+      "option-fire-and-ice-factions/ice            0.039872\n",
+      "option-fire-and-ice-final-scoring           0.039481\n",
+      "option-fire-and-ice-factions/volcano        0.039336\n",
+      "option-fire-and-ice-factions/variable_v5    0.024844\n",
+      "option-maintain-player-order                0.021085\n",
+      "option-fire-and-ice-factions/variable_v3    0.004580\n",
+      "option-fire-and-ice-factions/variable       0.003220\n",
+      "option-fire-and-ice-factions/variable_v4    0.002017\n",
+      "option-fire-and-ice-factions/variable_v2    0.001303\n",
+      "option-loose-adjust-resource                0.000012\n",
+      "Name: option, dtype: float64\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>game</th>\n",
+       "      <th>option</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0000000001</td>\n",
+       "      <td>option-strict-darkling-sh</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0000000001</td>\n",
+       "      <td>option-email-notify</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0000000001</td>\n",
+       "      <td>option-shipping-bonus</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0000000001</td>\n",
+       "      <td>option-strict-chaosmagician-sh</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0000000001</td>\n",
+       "      <td>option-errata-cultist-power</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1075569</th>\n",
+       "      <td>ZeicheMasZeuchnis</td>\n",
+       "      <td>option-email-notify</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1075570</th>\n",
+       "      <td>ZeicheMasZeuchnis</td>\n",
+       "      <td>option-strict-chaosmagician-sh</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1075571</th>\n",
+       "      <td>ZeicheMasZeuchnis</td>\n",
+       "      <td>option-errata-cultist-power</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1075572</th>\n",
+       "      <td>ZeicheMasZeuchnis</td>\n",
+       "      <td>option-mini-expansion-1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1075573</th>\n",
+       "      <td>ZeicheMasZeuchnis</td>\n",
+       "      <td>option-strict-leech</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1075574 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      game                          option\n",
+       "0               0000000001       option-strict-darkling-sh\n",
+       "1               0000000001             option-email-notify\n",
+       "2               0000000001           option-shipping-bonus\n",
+       "3               0000000001  option-strict-chaosmagician-sh\n",
+       "4               0000000001     option-errata-cultist-power\n",
+       "...                    ...                             ...\n",
+       "1075569  ZeicheMasZeuchnis             option-email-notify\n",
+       "1075570  ZeicheMasZeuchnis  option-strict-chaosmagician-sh\n",
+       "1075571  ZeicheMasZeuchnis     option-errata-cultist-power\n",
+       "1075572  ZeicheMasZeuchnis         option-mini-expansion-1\n",
+       "1075573  ZeicheMasZeuchnis             option-strict-leech\n",
+       "\n",
+       "[1075574 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(len(pd.unique(gameoptions['option'])))\n",
+    "print(gameoptions['option'].value_counts() / len(gameoptions))\n",
+    "gameoptions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "vp              12839907\n",
+       "build:D          6570095\n",
+       "leech:count      5716489\n",
+       "leech:pw         5716489\n",
+       "upgrade:TP       3543848\n",
+       "                  ...   \n",
+       "action:ACTH5        1852\n",
+       "order:6              912\n",
+       "order:7              636\n",
+       "action:ACTH4         592\n",
+       "action:ACTH3         314\n",
+       "Name: event, Length: 91, dtype: int64"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gameevents['event'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "126fe960806d587c78546b30f1a90853b1ada468    77158\n",
+       "95a66999127893f5925a5f591d54f8bcb9a670e6    21052\n",
+       "be8f6ebf549404d015547152d5f2a1906ae8dd90    16184\n",
+       "fdb13a13cd48b7a3c3525f27e4628ff6905aa5b1     1210\n",
+       "224736500d20520f195970eb0fd4c41df040c08c        3\n",
+       "735b073fd7161268bb2796c1275abda92acd8b1a        2\n",
+       "30b6ded823e53670624981abdb2c5b8568a44091        1\n",
+       "c07f36f9e050992d2daf6d44af2bc51dca719c46        1\n",
+       "b109f78907d2cbd5699ced16572be46043558e41        1\n",
+       "Name: base_map, dtype: int64"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "games['base_map'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[126fe960806d587c78546b30f1a90853b1ada468](https://terra.snellman.net/map/126fe960806d587c78546b30f1a90853b1ada468)\n",
+    "is the same as [91645cdb135773c2a7a50e5ca9cb18af54c664c4](https://terra.snellman.net/mapedit/91645cdb135773c2a7a50e5ca9cb18af54c664c4)  \n",
+    "[95a66999127893f5925a5f591d54f8bcb9a670e6](https://terra.snellman.net/map/95a66999127893f5925a5f591d54f8bcb9a670e6) is different  \n",
+    "[be8f6ebf549404d015547152d5f2a1906ae8dd90](https://terra.snellman.net/map/be8f6ebf549404d015547152d5f2a1906ae8dd90) is re-balanced.  \n",
+    "\n",
+    "All other maps aren't present here so we'll use those"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating the new datasets - some functions to help us make these later <a name=\"datasetCreate\"></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Victory Points (target) dataset <a name=\"VPData\"></a>\n",
+    "Let's make some functions to help make the dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def makenewdf():\n",
+    "    \"\"\"make an empty dataframe, organised in the way we want the target data, ready to be populated\"\"\"\n",
+    "    validfactions = ['witches', 'auren', 'swarmlings', 'mermaids', 'cultists', 'halflings', 'dwarves', 'engineers', 'chaosmagicians', 'giants', 'fakirs', 'nomads', 'darklings', 'alchemists']\n",
+    "    dfcols = ['game'] + validfactions\n",
+    "    vpdf = pd.DataFrame(columns=dfcols)\n",
+    "    \n",
+    "    return vpdf, dfcols, validfactions\n",
+    "\n",
+    "vpdf, dfcols, validfactions = makenewdf()\n",
+    "\n",
+    "sensecheck = False\n",
+    "if sensecheck:\n",
+    "    newdf = pd.DataFrame([[np.nan] * 15], columns=dfcols)\n",
+    "    newdf['auren'].replace({np.nan: 'test'}, inplace=True)\n",
+    "    vpdf = vpdf.append(newdf, ignore_index=True)\n",
+    "    print(len(vpdf))\n",
+    "    vpdf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_vp_from_game(singleGameEvents):\n",
+    "    \"\"\"Input game events for a single game. This is a pd.DataFrame. \n",
+    "    Output a row where each faction in the game has its vp populated (the rest are nans)\n",
+    "    \"\"\"\n",
+    "    newdf = pd.DataFrame([[np.nan] * 15], columns=dfcols)\n",
+    "    \n",
+    "    # assign the game number\n",
+    "    gameno = list(pd.unique(singleGameEvents['game']))\n",
+    "    \n",
+    "    # assert len(gameno) == 1, 'More than 1 unique game was found'    \n",
+    "    try:\n",
+    "        newdf['game'].replace({np.nan: gameno[0]}, inplace=True)\n",
+    "    except:\n",
+    "        print(f'DEBUGGING: len of table is {len(singleGameEvents)}')\n",
+    "        print(f'DEBUGGING: gamnos list: {gameno}')\n",
+    "        print(singleGameEvents)\n",
+    "        raise\n",
+    "        \n",
+    "    # find factions - there are some artifacts in the data. E.g. the \"faction\", \"all\". We need to filter them out.\n",
+    "    rawfactions = list(pd.unique(singleGameEvents['faction']))\n",
+    "    verifiedfactions = [rawfaction for rawfaction in rawfactions if rawfaction in validfactions]\n",
+    "    \n",
+    "    for faction in verifiedfactions:\n",
+    "        vpfaction = sum(singleGameEvents[(singleGameEvents['event'] == 'vp') & (singleGameEvents['faction'] == faction)]['num'])\n",
+    "        newdf[faction].replace({np.nan: vpfaction}, inplace=True)\n",
+    "        \n",
+    "    return newdf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time elapsed: 3.7775845527648926s\n",
+      "Time to do all games at current speed: 121.31502925395965hrs\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>game</th>\n",
+       "      <th>witches</th>\n",
+       "      <th>auren</th>\n",
+       "      <th>swarmlings</th>\n",
+       "      <th>mermaids</th>\n",
+       "      <th>cultists</th>\n",
+       "      <th>halflings</th>\n",
+       "      <th>dwarves</th>\n",
+       "      <th>engineers</th>\n",
+       "      <th>chaosmagicians</th>\n",
+       "      <th>giants</th>\n",
+       "      <th>fakirs</th>\n",
+       "      <th>nomads</th>\n",
+       "      <th>darklings</th>\n",
+       "      <th>alchemists</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0000000001</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>118.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>62.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         game  witches  auren  swarmlings  mermaids  cultists  halflings  \\\n",
+       "0  0000000001      NaN    NaN         NaN     118.0       NaN       62.0   \n",
+       "\n",
+       "   dwarves  engineers  chaosmagicians  giants  fakirs  nomads  darklings  \\\n",
+       "0      NaN        NaN             NaN     NaN     NaN     NaN        NaN   \n",
+       "\n",
+       "   alchemists  \n",
+       "0         NaN  "
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# sense check\n",
+    "start = time.time()\n",
+    "singlegame = gameevents[gameevents['game'] == '0000000001']\n",
+    "singlegamefactions = (list(pd.unique(singlegame['faction'])))\n",
+    "vpforgame = get_vp_from_game(singlegame)\n",
+    "end = time.time()\n",
+    "\n",
+    "print(f'Time elapsed: {end-start}s')\n",
+    "print(f'Time to do all games at current speed: {((end-start)*len(pd.unique(gameevents[\"game\"]))/3600)}hrs')\n",
+    "vpforgame"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Features dataset <a name=\"featuresData\"></a>\n",
+    "We'll want:\n",
+    "1. Boolean of bonus tiles (in game or not)  <span style=\"color:green\">- DONE</span>\n",
+    "2. One-hot of round tiles, for each round  <span style=\"color:green\">- DONE</span>\n",
+    "3. Boolean of factions already picked (more info if any have been picked yet) <span style=\"color:orange\">- removed as would require 1 row for each faction. We want 1 line per game for now</span>\n",
+    "4. One-hot for different map variants we'd like to use  <span style=\"color:green\">- DONE</span>\n",
+    "5. One-hot player count (from 2, 3, 4 or 5 players)  <span style=\"color:green\">- DONE</span>"
+   ]
+  },
+  {
+   "attachments": {
+    "942cd242-5b8b-432f-8843-51185679848e.png": {
+     "image/png": "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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These are the bonus tiles available:  \n",
+    "![image.png](attachment:942cd242-5b8b-432f-8843-51185679848e.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['pass:BON10', 'build:D', 'pass:BON3'], dtype=object)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.unique(gameevents[(gameevents['round'] == 0) & (gameevents['game'] == 'ytuwertqwtr')]['event'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But it looks like we only have details on the bonus tiles selected in the middle of the game. Where can we get information about all bonus tiles in game!? I'm not sure, so I'll use the only info that's available - which is the bonus tiles that players select. However, if there's a bonus tile that was never selected during a game, it won't be shown. If we go onto the Snellman website and re-watch an old game, we can see what bonus tiles are available in the GUI, so this info must be stored somewhere (as game history GUI must be using the same data we're looking at...?)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time elapsed: 3.7590675354003906s\n",
+      "['BON8', 'BON6', 'BON3', 'BON10', 'BON9']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# working out the code to get the bonus tiles & timing this\n",
+    "start = time.time()\n",
+    "allevents = list(pd.unique(gameevents[gameevents['game'] == 'ytuwertqwtr']['event']))\n",
+    "bonustiles = [event[5:] for event in allevents if event.startswith('pass:BON')]\n",
+    "end = time.time()\n",
+    "print(f'Time elapsed: {end-start}s')\n",
+    "print(bonustiles)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['game', 'r1_SCORE1', 'r1_SCORE2', 'r1_SCORE3', 'r1_SCORE4', 'r1_SCORE5', 'r1_SCORE6', 'r1_SCORE7', 'r1_SCORE8', 'r1_SCORE9', 'r2_SCORE1', 'r2_SCORE2', 'r2_SCORE3', 'r2_SCORE4', 'r2_SCORE5', 'r2_SCORE6', 'r2_SCORE7', 'r2_SCORE8', 'r2_SCORE9', 'r3_SCORE1', 'r3_SCORE2', 'r3_SCORE3', 'r3_SCORE4', 'r3_SCORE5', 'r3_SCORE6', 'r3_SCORE7', 'r3_SCORE8', 'r3_SCORE9', 'r4_SCORE1', 'r4_SCORE2', 'r4_SCORE3', 'r4_SCORE4', 'r4_SCORE5', 'r4_SCORE6', 'r4_SCORE7', 'r4_SCORE8', 'r4_SCORE9', 'r5_SCORE1', 'r5_SCORE2', 'r5_SCORE3', 'r5_SCORE4', 'r5_SCORE5', 'r5_SCORE6', 'r5_SCORE7', 'r5_SCORE8', 'r5_SCORE9', 'r6_SCORE1', 'r6_SCORE2', 'r6_SCORE3', 'r6_SCORE4', 'r6_SCORE5', 'r6_SCORE6', 'r6_SCORE7', 'r6_SCORE8', 'r6_SCORE9', 'BON1', 'BON2', 'BON3', 'BON4', 'BON5', 'BON6', 'BON7', 'BON8', 'BON9', 'BON10', '2players', '3players', '4players', '5players', 'map1', 'map2', 'map3']\n",
+      "72\n"
+     ]
+    }
+   ],
+   "source": [
+    "def emptyfeaturesdf():\n",
+    "    \"\"\"make an empty dataframe, organised in the way we want the feature data, ready to be populated\"\"\"\n",
+    "    colnames = ['game']\n",
+    "    uniqueScoreTiles = np.sort(pd.unique(gamescoringtiles['tile']))\n",
+    "    \n",
+    "    # One-hot of round tiles, for each round\n",
+    "    for gameround in range(1, 7):\n",
+    "        roundstr = f'r{gameround}'\n",
+    "        for tile in uniqueScoreTiles:\n",
+    "            colnames.append(roundstr + '_' + tile)\n",
+    "    \n",
+    "    # Boolean of bonus tiles\n",
+    "    for bon in range(1, 11):\n",
+    "        colnames.append(f'BON{bon}')\n",
+    "        \n",
+    "    # One-hot player count (from 2, 3, 4 or 5 players)\n",
+    "    for player in range(2, 6):\n",
+    "        colnames.append(f'{player}players')\n",
+    "    \n",
+    "    # one hot of the map used\n",
+    "    \"\"\"126fe960806d587c78546b30f1a90853b1ada468 - map1\n",
+    "       95a66999127893f5925a5f591d54f8bcb9a670e6 - map2\n",
+    "       be8f6ebf549404d015547152d5f2a1906ae8dd90 - map3\n",
+    "    \"\"\"\n",
+    "    colnames = colnames + ['map1', 'map2', 'map3']\n",
+    "    \n",
+    "    featuresdf = pd.DataFrame(columns=colnames)\n",
+    "    \n",
+    "    return featuresdf, colnames\n",
+    "\n",
+    "featuresdf, featcolnames = emptyfeaturesdf()\n",
+    "print(featcolnames)\n",
+    "print(len(featcolnames))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_features_from_game(singlegameevents, singlegamemeta, singlegameST, singleendplayers=None):\n",
+    "    \"\"\"\n",
+    "    Inputs:\n",
+    "        singlegameevents <pd.DataFrame>   - is game events for a single game \n",
+    "        singlegamemeta   <pd.DataFrame>   - is a single row from `games` that gives map & player count\n",
+    "        singlegameST     <pd.DataFrame>   - is a single row from `gamescoringtiles` that gives... score tile (suprisingly)\n",
+    "        singleendplayers <pd.DataFrame>   - is a single row from `end players` that gives the amount of players at end of game, after dropouts\n",
+    "    Return:          <pd.DataFrame>   - a row where features have been found (will be sparse)\n",
+    "    \"\"\"\n",
+    "    newdf = pd.DataFrame([[0] * len(featcolnames)], columns=featcolnames)\n",
+    "    \n",
+    "    # assign game string\n",
+    "    singlegamemeta.iloc[0]['game']\n",
+    "    newdf['game'].replace({0: singlegamemeta.iloc[0]['game']}, inplace=True)\n",
+    "    \n",
+    "    # find the round tiles for each round\n",
+    "    for gameround in range(1, 7):\n",
+    "        roundstr = f'r{gameround}'\n",
+    "        scoretile = roundstr + '_' + singlegameST[singlegameST['round'] == gameround]['tile'].values[0]\n",
+    "        newdf[scoretile].replace({0: 1}, inplace=True)\n",
+    "        \n",
+    "    # Boolean of bonus tiles\n",
+    "    uniqueevents = list(pd.unique(singlegameevents['event']))\n",
+    "    bonustiles = [event[5:] for event in uniqueevents if event.startswith('pass:BON')]\n",
+    "    for bontile in bonustiles:\n",
+    "        newdf[bontile].replace({0: 1}, inplace=True)\n",
+    "       \n",
+    "    # One-hot player count (from 2, 3, 4 or 5 players)\n",
+    "    if singleendplayers is None:\n",
+    "        noplayers = singlegamemeta.iloc[0]['player_count']\n",
+    "        print('gamemeta used for player count')\n",
+    "    else:\n",
+    "        noplayers = singleendplayers.iloc[0]['endplayers']\n",
+    "        \n",
+    "    players = f'{noplayers}players'\n",
+    "    newdf[players].replace({0: 1}, inplace=True)\n",
+    "          \n",
+    "    # one hot of the map used\n",
+    "    mapdict = {'126fe960806d587c78546b30f1a90853b1ada468': 'map1',\n",
+    "               '95a66999127893f5925a5f591d54f8bcb9a670e6': 'map2',\n",
+    "               'be8f6ebf549404d015547152d5f2a1906ae8dd90': 'map3'\n",
+    "              }\n",
+    "    basemap = singlegamemeta.iloc[0]['base_map']\n",
+    "    gamemap = mapdict[basemap]\n",
+    "    newdf[gamemap].replace({0: 1}, inplace=True)\n",
+    "        \n",
+    "    return newdf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gamemeta used for player count\n",
+      "Time elapsed: 4.1567041873931885s\n",
+      "Time to do all games at current speed: 133.49024569802813hrs\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>game</th>\n",
+       "      <th>r1_SCORE1</th>\n",
+       "      <th>r1_SCORE2</th>\n",
+       "      <th>r1_SCORE3</th>\n",
+       "      <th>r1_SCORE4</th>\n",
+       "      <th>r1_SCORE5</th>\n",
+       "      <th>r1_SCORE6</th>\n",
+       "      <th>r1_SCORE7</th>\n",
+       "      <th>r1_SCORE8</th>\n",
+       "      <th>r1_SCORE9</th>\n",
+       "      <th>...</th>\n",
+       "      <th>BON8</th>\n",
+       "      <th>BON9</th>\n",
+       "      <th>BON10</th>\n",
+       "      <th>2players</th>\n",
+       "      <th>3players</th>\n",
+       "      <th>4players</th>\n",
+       "      <th>5players</th>\n",
+       "      <th>map1</th>\n",
+       "      <th>map2</th>\n",
+       "      <th>map3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0000000001</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 72 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         game  r1_SCORE1  r1_SCORE2  r1_SCORE3  r1_SCORE4  r1_SCORE5  \\\n",
+       "0  0000000001          0          0          0          0          0   \n",
+       "\n",
+       "   r1_SCORE6  r1_SCORE7  r1_SCORE8  r1_SCORE9  ...  BON8  BON9  BON10  \\\n",
+       "0          0          0          1          0  ...     0     1      1   \n",
+       "\n",
+       "   2players  3players  4players  5players  map1  map2  map3  \n",
+       "0         1         0         0         0     1     0     0  \n",
+       "\n",
+       "[1 rows x 72 columns]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# sense check\n",
+    "start = time.time()\n",
+    "singlegame = gameevents[gameevents['game'] == '0000000001']\n",
+    "singlegamemeta = games[games['game'] == '0000000001']\n",
+    "singlegameST = gamescoringtiles[gamescoringtiles['game'] == '0000000001']\n",
+    "vpforgame = get_vp_from_game(singlegame)\n",
+    "featsforgame = get_features_from_game(singlegame, singlegamemeta, singlegameST)\n",
+    "end = time.time()\n",
+    "\n",
+    "print(f'Time elapsed: {end-start}s')\n",
+    "print(f'Time to do all games at current speed: {((end-start)*len(pd.unique(gameevents[\"game\"]))/3600)}hrs')\n",
+    "featsforgame"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Speeding up dataset creation <a name=\"speedUp\"></a>\n",
+    "350 hours: that's way too long. How long will it take to split the table into 1500, 100 game tables and work on those?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time elapsed to make small table: 1.3441839218139648s\n",
+      "gamemeta used for player count\n",
+      "Time elapsed to get data for single game now: 0.02479243278503418s\n",
+      "Time elapsed to do all small tables: 0.4316771988021003hrs\n",
+      "Time elapsed to do all data creation: 0.7961952053176032hrs\n",
+      "Time elapsed to do all processing: 1.2278724041197036hrs\n"
+     ]
+    }
+   ],
+   "source": [
+    "first100games = gameslist[:100]\n",
+    "\n",
+    "start = time.time()\n",
+    "gameevents100 = gameevents[gameevents['game'].isin(first100games)]\n",
+    "gamemeta100 = games[games['game'].isin(first100games)]\n",
+    "gameST100 = gamescoringtiles[gamescoringtiles['game'].isin(first100games)]\n",
+    "end = time.time()\n",
+    "smalltablestime = end-start\n",
+    "\n",
+    "print(f'Time elapsed to make small table: {smalltablestime}s')\n",
+    "\n",
+    "start = time.time()\n",
+    "singlegame = gameevents100[gameevents100['game'] == '0000000001']\n",
+    "singlegamemeta = gamemeta100[gamemeta100['game'] == '0000000001']\n",
+    "singlegameST = gameST100[gameST100['game'] == '0000000001']\n",
+    "vpforgame = get_vp_from_game(singlegame)\n",
+    "featsforgame = get_features_from_game(singlegame, singlegamemeta, singlegameST)\n",
+    "end = time.time()\n",
+    "datacreatetime = end-start\n",
+    "\n",
+    "print(f'Time elapsed to get data for single game now: {end-start}s')\n",
+    "totalsmalltablestime = smalltablestime * len(pd.unique(gameevents[\"game\"]))/(100 * 3600)  # divide by 100 as that's the size of table, divide by 3600 for s -> hrs\n",
+    "totaldatacreatetime = datacreatetime * len(pd.unique(gameevents[\"game\"]))/3600\n",
+    "print(f'Time elapsed to do all small tables: {totalsmalltablestime}hrs')\n",
+    "print(f'Time elapsed to do all data creation: {totaldatacreatetime}hrs')\n",
+    "print(f'Time elapsed to do all processing: {totalsmalltablestime + totaldatacreatetime}hrs')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Damn. I've just saved ~200 times the amount of time by doing that. As a complete aside, for pure interest, I wonder what the best trade-off between spitting into smaller & smaller tables (which increases time to do this) is vs saving time to then filter with smaller tables, is? (will check this out later)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Progressed to 100th game\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "Progressed to 200th game\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "no of unique games in table is: 200\n"
+     ]
+    },
+    {
+     "data": {
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0512</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>124.0</td>\n",
+       "      <td>101.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>051501</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>84.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>78.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>060303</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>108.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>121.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>78.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>195</th>\n",
+       "      <td>4pLeague_S7_D6L17_G7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>73.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>130.0</td>\n",
+       "      <td>87.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>196</th>\n",
+       "      <td>4pLeague_S7_D6L18_G2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>108.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>197</th>\n",
+       "      <td>4pLeague_S7_D6L18_G5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>82.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>110.0</td>\n",
+       "      <td>117.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>198</th>\n",
+       "      <td>4pLeague_S7_D6L18_G6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>78.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>103.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>137.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>61.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>199</th>\n",
+       "      <td>4pLeague_S7_D6L18_G7</td>\n",
+       "      <td>95.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>200 rows × 15 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     game  witches  auren  swarmlings  mermaids  cultists  \\\n",
+       "0              0000000001      NaN    NaN         NaN     118.0       NaN   \n",
+       "1                  050615      NaN    NaN         NaN       NaN       NaN   \n",
+       "2                    0512      NaN    NaN         NaN       NaN       NaN   \n",
+       "3                  051501      NaN   84.0         NaN       NaN       NaN   \n",
+       "4                  060303      NaN    NaN         NaN     108.0       NaN   \n",
+       "..                    ...      ...    ...         ...       ...       ...   \n",
+       "195  4pLeague_S7_D6L17_G7      NaN    NaN        48.0       NaN       NaN   \n",
+       "196  4pLeague_S7_D6L18_G2      NaN  105.0         NaN       NaN       NaN   \n",
+       "197  4pLeague_S7_D6L18_G5      NaN    NaN        97.0       NaN       NaN   \n",
+       "198  4pLeague_S7_D6L18_G6      NaN    NaN        78.0       NaN       NaN   \n",
+       "199  4pLeague_S7_D6L18_G7     95.0    NaN         NaN       NaN       NaN   \n",
+       "\n",
+       "     halflings  dwarves  engineers  chaosmagicians  giants  fakirs  nomads  \\\n",
+       "0         62.0      NaN        NaN             NaN     NaN     NaN     NaN   \n",
+       "1          NaN      NaN        NaN             NaN     NaN     NaN     NaN   \n",
+       "2          NaN      NaN      124.0           101.0     NaN     NaN     NaN   \n",
+       "3         77.0      NaN        NaN            72.0     NaN     NaN    54.0   \n",
+       "4          NaN      NaN      121.0             NaN     NaN    78.0     NaN   \n",
+       "..         ...      ...        ...             ...     ...     ...     ...   \n",
+       "195        NaN      NaN        NaN            73.0     NaN     NaN   130.0   \n",
+       "196        NaN      NaN        NaN           108.0     NaN     NaN   128.0   \n",
+       "197        NaN     82.0        NaN             NaN     NaN     NaN   110.0   \n",
+       "198      103.0      NaN        NaN           137.0     NaN     NaN     NaN   \n",
+       "199      122.0      NaN        NaN            90.0     NaN     NaN     NaN   \n",
+       "\n",
+       "     darklings  alchemists  \n",
+       "0          NaN         NaN  \n",
+       "1        122.0         NaN  \n",
+       "2          NaN         NaN  \n",
+       "3         78.0         NaN  \n",
+       "4        128.0         NaN  \n",
+       "..         ...         ...  \n",
+       "195       87.0         NaN  \n",
+       "196       53.0         NaN  \n",
+       "197      117.0         NaN  \n",
+       "198       61.0         NaN  \n",
+       "199      120.0         NaN  \n",
+       "\n",
+       "[200 rows x 15 columns]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# sense check for 100 games\n",
+    "vpdf, _, _ = makenewdf()\n",
+    "featdf, _ = emptyfeaturesdf()\n",
+    "gamelengthlen = len(gameslist)\n",
+    "gamesroundup = math.ceil(gamelengthlen / 100.0) * 100\n",
+    "jj = 0\n",
+    "\n",
+    "for ii in range(100, 201, 100):     \n",
+    "    ii = min(ii, gamelengthlen)    \n",
+    "    print(f'Progressed to {ii}th game')\n",
+    "    \n",
+    "    next100games = gameslist[jj:ii]\n",
+    "    jj = ii  # so that we don't get any repetitions at the very end, where our set will be smaller\n",
+    "    \n",
+    "    gameevents100 = gameevents[gameevents['game'].isin(next100games)]\n",
+    "    gamemeta100 = games[games['game'].isin(next100games)]\n",
+    "    gameST100 = gamescoringtiles[gamescoringtiles['game'].isin(next100games)]\n",
+    "    \n",
+    "    for game in next100games:\n",
+    "        singlegame = gameevents100[gameevents100['game'] == game]\n",
+    "        singlegamemeta = gamemeta100[gamemeta100['game'] == game]\n",
+    "        singlegameST = gameST100[gameST100['game'] == game]\n",
+    "        \n",
+    "        if not len(singlegame) == 0:        \n",
+    "            vpforgame = get_vp_from_game(singlegame)\n",
+    "            featsforgame = get_features_from_game(singlegame, singlegamemeta, singlegameST)\n",
+    "            \n",
+    "            vpdf = vpdf.append(vpforgame, ignore_index=True)\n",
+    "            featdf = featdf.append(featsforgame, ignore_index=True)\n",
+    "    \n",
+    "print(f\"no of unique games in table is: {len(list(pd.unique(vpdf['game'])))}\")\n",
+    "vpdf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>game</th>\n",
+       "      <th>r1_SCORE1</th>\n",
+       "      <th>r1_SCORE2</th>\n",
+       "      <th>r1_SCORE3</th>\n",
+       "      <th>r1_SCORE4</th>\n",
+       "      <th>r1_SCORE5</th>\n",
+       "      <th>r1_SCORE6</th>\n",
+       "      <th>r1_SCORE7</th>\n",
+       "      <th>r1_SCORE8</th>\n",
+       "      <th>r1_SCORE9</th>\n",
+       "      <th>...</th>\n",
+       "      <th>BON8</th>\n",
+       "      <th>BON9</th>\n",
+       "      <th>BON10</th>\n",
+       "      <th>2players</th>\n",
+       "      <th>3players</th>\n",
+       "      <th>4players</th>\n",
+       "      <th>5players</th>\n",
+       "      <th>map1</th>\n",
+       "      <th>map2</th>\n",
+       "      <th>map3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0000000001</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>050615</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0512</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>051501</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>060303</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>195</th>\n",
+       "      <td>4pLeague_S7_D6L17_G7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>196</th>\n",
+       "      <td>4pLeague_S7_D6L18_G2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>197</th>\n",
+       "      <td>4pLeague_S7_D6L18_G5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>198</th>\n",
+       "      <td>4pLeague_S7_D6L18_G6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>199</th>\n",
+       "      <td>4pLeague_S7_D6L18_G7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>200 rows × 72 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     game r1_SCORE1 r1_SCORE2 r1_SCORE3 r1_SCORE4 r1_SCORE5  \\\n",
+       "0              0000000001         0         0         0         0         0   \n",
+       "1                  050615         0         0         0         0         1   \n",
+       "2                    0512         0         0         0         0         1   \n",
+       "3                  051501         0         0         0         0         0   \n",
+       "4                  060303         0         1         0         0         0   \n",
+       "..                    ...       ...       ...       ...       ...       ...   \n",
+       "195  4pLeague_S7_D6L17_G7         0         0         0         0         0   \n",
+       "196  4pLeague_S7_D6L18_G2         0         0         0         0         0   \n",
+       "197  4pLeague_S7_D6L18_G5         0         0         0         0         0   \n",
+       "198  4pLeague_S7_D6L18_G6         0         0         0         0         0   \n",
+       "199  4pLeague_S7_D6L18_G7         0         0         0         0         0   \n",
+       "\n",
+       "    r1_SCORE6 r1_SCORE7 r1_SCORE8 r1_SCORE9  ... BON8 BON9 BON10 2players  \\\n",
+       "0           0         0         1         0  ...    0    1     1        1   \n",
+       "1           0         0         0         0  ...    1    1     0        0   \n",
+       "2           0         0         0         0  ...    1    1     0        0   \n",
+       "3           0         0         1         0  ...    1    0     0        0   \n",
+       "4           0         0         0         0  ...    1    1     1        0   \n",
+       "..        ...       ...       ...       ...  ...  ...  ...   ...      ...   \n",
+       "195         0         1         0         0  ...    1    1     1        0   \n",
+       "196         0         1         0         0  ...    1    0     1        0   \n",
+       "197         0         0         1         0  ...    1    1     0        0   \n",
+       "198         0         0         1         0  ...    1    0     1        0   \n",
+       "199         0         0         1         0  ...    1    1     1        0   \n",
+       "\n",
+       "    3players 4players 5players map1 map2 map3  \n",
+       "0          0        0        0    1    0    0  \n",
+       "1          0        1        0    0    0    1  \n",
+       "2          0        1        0    1    0    0  \n",
+       "3          0        0        1    1    0    0  \n",
+       "4          0        1        0    0    1    0  \n",
+       "..       ...      ...      ...  ...  ...  ...  \n",
+       "195        0        1        0    1    0    0  \n",
+       "196        0        1        0    1    0    0  \n",
+       "197        0        1        0    1    0    0  \n",
+       "198        0        1        0    1    0    0  \n",
+       "199        0        1        0    1    0    0  \n",
+       "\n",
+       "[200 rows x 72 columns]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "featdf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Looks good! We can soon do it with the full set of data... after we've done some cleaning.\n",
+    "\n",
+    "## Cleaning and filtering the data <a name=\"cleanUp\"></a>  \n",
+    "\n",
+    "Filtering bad data. We'll need to check... \n",
+    "1. gameevents have the same game list as games <span style=\"color:green\">- DONE</span>\n",
+    "2. the game has finished <span style=\"color:green\"> - DONE</span><span style=\"color:orange\"> -  more naunced: dropouts will change the feature, \"no. of players\". Therefore \"no. of players\" will be assigned to the end no. of players (after dropout) </span>\n",
+    "3. the map used (only use a sub-set of maps, or we could one-hot a list of accepted maps in X (features) <span style=\"color:green\">- DONE</span>\n",
+    "4. additional rules? Look up <span style=\"color:orange\">- left for now as v bitty: will add a bit of noise but not a lot</span>\n",
+    "5. Number of players. Possible for 2, 3, 4 or 5 player games, we could one-hot the differences as there might be similarities in different players that can contribute to predictions between different player games. Although >5 players should be removed. <span style=\"color:green\"> - DONE</span>\n",
+    "6. No extra factions (shapeshifters etc)   <span style=\"color:orange\"> - more naunced: these will be filtered out at the stage of making the data, using \"valid factions\". However we don't want to filter out any games where a valid faction is playing against an extra faction (we just won't include that faction's vp data). So we include it in the \"player dropped\" group.</span>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = dict()\n",
+    "data['gameevents'] = gameevents\n",
+    "data['games'] = games\n",
+    "data['gamescoringtiles'] = gamescoringtiles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def filteringByBadgames(data, badgames):\n",
+    "    \"\"\" Data is a dict containing gameevents, games, gamescoringtiles\n",
+    "        badgames is a pd.dataframe that contains ['game'] to filter by\n",
+    "    \"\"\"\n",
+    "    gameeventsfil = data['gameevents']\n",
+    "    gamesfil = data['games']\n",
+    "    gamescoringtilesfil = data['gamescoringtiles']\n",
+    "    \n",
+    "    badgameslist = badgames['game']\n",
+    "    gameeventsfilbefore = len(gameeventsfil)\n",
+    "    gamesbefore = len(gamesfil)\n",
+    "    gameSTbefore = len(gamescoringtilesfil)\n",
+    "\n",
+    "    gameeventsfil = gameeventsfil[~gameeventsfil['game'].isin(badgameslist)]\n",
+    "    gamesfil = gamesfil[~gamesfil['game'].isin(badgameslist)]\n",
+    "    gamescoringtilesfil = gamescoringtilesfil[~gamescoringtilesfil['game'].isin(badgameslist)]\n",
+    "\n",
+    "    print(f'game events before: {gameeventsfilbefore}, game events after: {len(gameeventsfil)}, game events removed: {gameeventsfilbefore-len(gameeventsfil)}')\n",
+    "    print(f'games before: {gamesbefore}, games after: {len(gamesfil)}, games removed: {gamesbefore-len(gamesfil)}')\n",
+    "    print(f'gameST before: {gameSTbefore}, gameST after: {len(gamescoringtilesfil)}, games removed: {gameSTbefore-len(gamescoringtilesfil)}')\n",
+    "    \n",
+    "    data['gameevents'] = gameeventsfil\n",
+    "    data['games'] = gamesfil\n",
+    "    data['gamescoringtiles'] = gamescoringtilesfil\n",
+    "    \n",
+    "    return data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### `Gameevents` games are same as `games` games"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# games from game_events are in games:\n",
+    "gamesymmetricdif = set(gameslist) ^ set(games['game'])\n",
+    "len(gamesymmetricdif)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So we have all the same games. Cool."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Removing the 1, 6, 7 player games\n",
+    "This was a problem noted here: https://www.kaggle.com/lemonkoala/some-faulty-data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>base_map</th>\n",
+       "      <th>game</th>\n",
+       "      <th>last_update</th>\n",
+       "      <th>player_count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1267</th>\n",
+       "      <td>be8f6ebf549404d015547152d5f2a1906ae8dd90</td>\n",
+       "      <td>HenriMustGoDown</td>\n",
+       "      <td>2015-07-10 06:03:39</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3110</th>\n",
+       "      <td>be8f6ebf549404d015547152d5f2a1906ae8dd90</td>\n",
+       "      <td>7pmatch3</td>\n",
+       "      <td>2015-11-23 17:34:26</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3896</th>\n",
+       "      <td>be8f6ebf549404d015547152d5f2a1906ae8dd90</td>\n",
+       "      <td>GTMDHXD</td>\n",
+       "      <td>2015-11-02 05:54:08</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4760</th>\n",
+       "      <td>95a66999127893f5925a5f591d54f8bcb9a670e6</td>\n",
+       "      <td>seven11</td>\n",
+       "      <td>2015-11-26 10:38:09</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5728</th>\n",
+       "      <td>be8f6ebf549404d015547152d5f2a1906ae8dd90</td>\n",
+       "      <td>6playerGregvlwonthelastone</td>\n",
+       "      <td>2016-01-11 14:43:19</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>112011</th>\n",
+       "      <td>126fe960806d587c78546b30f1a90853b1ada468</td>\n",
+       "      <td>7pmatch</td>\n",
+       "      <td>2015-08-22 14:35:38</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>112930</th>\n",
+       "      <td>be8f6ebf549404d015547152d5f2a1906ae8dd90</td>\n",
+       "      <td>MEGA2KING</td>\n",
+       "      <td>2015-08-26 12:21:04</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>113411</th>\n",
+       "      <td>126fe960806d587c78546b30f1a90853b1ada468</td>\n",
+       "      <td>ScruffyLookingNerfHerder</td>\n",
+       "      <td>2015-08-18 02:13:18</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>113813</th>\n",
+       "      <td>be8f6ebf549404d015547152d5f2a1906ae8dd90</td>\n",
+       "      <td>youmi023</td>\n",
+       "      <td>2015-08-27 08:07:33</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>113846</th>\n",
+       "      <td>126fe960806d587c78546b30f1a90853b1ada468</td>\n",
+       "      <td>YutoriGoGo009</td>\n",
+       "      <td>2015-08-29 00:32:15</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>83 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                        base_map                        game  \\\n",
+       "1267    be8f6ebf549404d015547152d5f2a1906ae8dd90             HenriMustGoDown   \n",
+       "3110    be8f6ebf549404d015547152d5f2a1906ae8dd90                    7pmatch3   \n",
+       "3896    be8f6ebf549404d015547152d5f2a1906ae8dd90                     GTMDHXD   \n",
+       "4760    95a66999127893f5925a5f591d54f8bcb9a670e6                     seven11   \n",
+       "5728    be8f6ebf549404d015547152d5f2a1906ae8dd90  6playerGregvlwonthelastone   \n",
+       "...                                          ...                         ...   \n",
+       "112011  126fe960806d587c78546b30f1a90853b1ada468                     7pmatch   \n",
+       "112930  be8f6ebf549404d015547152d5f2a1906ae8dd90                   MEGA2KING   \n",
+       "113411  126fe960806d587c78546b30f1a90853b1ada468    ScruffyLookingNerfHerder   \n",
+       "113813  be8f6ebf549404d015547152d5f2a1906ae8dd90                    youmi023   \n",
+       "113846  126fe960806d587c78546b30f1a90853b1ada468               YutoriGoGo009   \n",
+       "\n",
+       "                last_update  player_count  \n",
+       "1267    2015-07-10 06:03:39             6  \n",
+       "3110    2015-11-23 17:34:26             7  \n",
+       "3896    2015-11-02 05:54:08             7  \n",
+       "4760    2015-11-26 10:38:09             7  \n",
+       "5728    2016-01-11 14:43:19             6  \n",
+       "...                     ...           ...  \n",
+       "112011  2015-08-22 14:35:38             7  \n",
+       "112930  2015-08-26 12:21:04             6  \n",
+       "113411  2015-08-18 02:13:18             6  \n",
+       "113813  2015-08-27 08:07:33             7  \n",
+       "113846  2015-08-29 00:32:15             7  \n",
+       "\n",
+       "[83 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "badgames = games[games[\"player_count\"].isin([1, 6, 7])]\n",
+    "badgames"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "game events before: 73419074, game events after: 73332500, game events removed: 86574\n",
+      "games before: 115612, games after: 115529, games removed: 83\n",
+      "gameST before: 693660, gameST after: 693162, games removed: 498\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = filteringByBadgames(data, badgames)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Checking the game has finished\n",
+    "To check this, we can look at game events. If every player has passed their bonus tile on round 6, they've all made it to the end. If not, they've dropped out."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['action:ACT1', 'action:ACT2', 'action:ACT4', 'action:FAV6',\n",
+       "       'advance:ship', 'bridge', 'build:D', 'burn', 'decline:count',\n",
+       "       'decline:pw', 'dig', 'favor:FAV12', 'favor:any', 'leech:count',\n",
+       "       'leech:pw', 'order:1', 'order:2', 'order:3', 'order:4', 'town:TW1',\n",
+       "       'town:TW2', 'town:TW5', 'town:any', 'upgrade:TE', 'upgrade:TP',\n",
+       "       'vp'], dtype=object)"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "singlegame = gameevents[gameevents['game'] == 'NewbiesWelcome12']\n",
+    "r6 = singlegame[singlegame['round'] == 6]\n",
+    "uniqueevents = np.sort(pd.unique(r6['event']))\n",
+    "uniqueevents"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There's no pass logged in round 6?! What?! What about round 5?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['action:ACT1', 'action:ACT3', 'action:ACT6', 'action:ACTS',\n",
+       "       'action:BON1', 'action:FAV6', 'bridge', 'build:D', 'burn', 'dig',\n",
+       "       'favor:FAV1', 'favor:FAV10', 'favor:FAV5', 'favor:FAV6',\n",
+       "       'favor:any', 'leech:count', 'leech:pw', 'order:1', 'order:2',\n",
+       "       'order:3', 'order:4', 'pass:BON2', 'pass:BON4', 'pass:BON7',\n",
+       "       'town:TW3', 'town:any', 'upgrade:SA', 'upgrade:TE', 'upgrade:TP',\n",
+       "       'vp'], dtype=object)"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "singlegame = gameevents[gameevents['game'] == 'NewbiesWelcome12']\n",
+    "r5 = singlegame[singlegame['round'] == 5]\n",
+    "uniqueevents = np.sort(pd.unique(r5['event']))\n",
+    "uniqueevents"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4388095    False\n",
+       "4388096    False\n",
+       "4388107    False\n",
+       "4388109    False\n",
+       "4388119    False\n",
+       "           ...  \n",
+       "4388605    False\n",
+       "4388607    False\n",
+       "4388612    False\n",
+       "4388618    False\n",
+       "4388619    False\n",
+       "Name: event, Length: 92, dtype: bool"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r6['event'].str.startswith('pass')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Okay. So there is in round 5 but not in round 6. Not a fool-proof way of checking no-one has dropped out but you'd hope if they made it to round 6, they wouldn't drop out then. Very annoying, we'll just base it off round 5 passes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "faction: swarmlings ended their turn?: True\n",
+      "faction: halflings ended their turn?: True\n",
+      "faction: engineers ended their turn?: False\n",
+      "faction: witches ended their turn?: True\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>game</th>\n",
+       "      <th>nodrops</th>\n",
+       "      <th>startplayers</th>\n",
+       "      <th>playersdropped</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>NewbiesWelcome12</td>\n",
+       "      <td>False</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               game  nodrops  startplayers  playersdropped\n",
+       "0  NewbiesWelcome12    False             4               1"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# sense-check - every player passed final round except engineers in this game\n",
+    "singlegame = gameevents[gameevents['game'] == 'NewbiesWelcome12']\n",
+    "\n",
+    "extendedfactions = validfactions + ['dragonlords', 'riverwalkers', 'yetis', 'icemaidens', 'shapeshifters', 'acolytes']\n",
+    "\n",
+    "def check_game_ended(singlegame, verbose=False):\n",
+    "    r5 = singlegame[singlegame['round'] == 5]\n",
+    "    rawfactions = pd.unique(singlegame['faction'])\n",
+    "    verifiedfactions = [rawfaction for rawfaction in rawfactions if rawfaction in extendedfactions]\n",
+    "    allbool = []\n",
+    "\n",
+    "    for faction in verifiedfactions:\n",
+    "        factionbool = len(r5[(r5['faction'] == faction)  & (r5['event'].str.startswith('pass'))]) == 1\n",
+    "        allbool.append(factionbool)\n",
+    "        \n",
+    "        if verbose:\n",
+    "            print(f'faction: {faction} ended their turn?: {factionbool}')\n",
+    "    \n",
+    "    isgood = all(allbool)\n",
+    "    startplayers = len(verifiedfactions)\n",
+    "    boolsum = sum(allbool)\n",
+    "    playersdropped = startplayers - boolsum\n",
+    "    \n",
+    "    return isgood, startplayers, playersdropped\n",
+    "\n",
+    "isgood, startplayers, playersdropped = check_game_ended(singlegame, verbose=True)\n",
+    "newdf = pd.DataFrame([['NewbiesWelcome12', isgood, startplayers, playersdropped]], columns=['game', 'nodrops', 'startplayers', 'playersdropped'])\n",
+    "newdf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll also need to check there even *is* a round 5 (or a round 6). If there isn't... clearly the game hasn't been finished. We might as well check nothing odd is going on and there isn't a round 7/8/9 either."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Progressed to 10000th game\n",
+      "Progressed to 20000th game\n",
+      "Progressed to 30000th game\n",
+      "Progressed to 40000th game\n",
+      "Progressed to 50000th game\n",
+      "Progressed to 60000th game\n",
+      "Progressed to 70000th game\n",
+      "Progressed to 80000th game\n",
+      "Progressed to 90000th game\n",
+      "Progressed to 100000th game\n",
+      "Progressed to 110000th game\n",
+      "no of unique games in table is: 115528\n"
+     ]
+    }
+   ],
+   "source": [
+    "gameevents = data['gameevents']\n",
+    "gameslist = list(pd.unique(gameevents['game']))\n",
+    "\n",
+    "gamelengthlen = len(gameslist)\n",
+    "gamesroundup = math.ceil(gamelengthlen / 100.0) * 100\n",
+    "jj = 0\n",
+    "playerdropdf = pd.DataFrame(columns=['game', 'nodrops', 'startplayers', 'playersdropped'])\n",
+    "\n",
+    "for ii in range(100, gamesroundup+1, 100):     \n",
+    "    ii = min(ii, gamelengthlen)    \n",
+    "    if (ii % 10000) == 0:  # update every 10000 games\n",
+    "        print(f'Progressed to {ii}th game')\n",
+    "    \n",
+    "    next100games = gameslist[jj:ii]\n",
+    "    jj = ii  # so that we don't get any repetitions at the very end, where our set will be smaller\n",
+    "    \n",
+    "    gameevents100 = gameevents[gameevents['game'].isin(next100games)]\n",
+    "\n",
+    "    for game in next100games:\n",
+    "        singlegame = gameevents100[gameevents100['game'] == game]\n",
+    "        \n",
+    "        if not len(singlegame) == 0:\n",
+    "            isgood, startplayers, playersdropped = check_game_ended(singlegame)\n",
+    "            newdf = pd.DataFrame([[game, isgood, startplayers, playersdropped]], columns=['game', 'nodrops', 'startplayers', 'playersdropped'])\n",
+    "            playerdropdf = playerdropdf.append(newdf, ignore_index=True)\n",
+    "\n",
+    "print(f\"no of unique games in table is: {len(list(pd.unique(playerdropdf['game'])))}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    100147\n",
+       "1     11975\n",
+       "2      2720\n",
+       "3       530\n",
+       "4       133\n",
+       "5        23\n",
+       "Name: playersdropped, dtype: int64"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "playerdropdf['playersdropped'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "playerdropdf['endplayers'] = playerdropdf['startplayers'] - playerdropdf['playersdropped']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4    57599\n",
+       "3    25708\n",
+       "2    23374\n",
+       "5     6057\n",
+       "1     1921\n",
+       "0      869\n",
+       "Name: endplayers, dtype: int64"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "playerdropdf['endplayers'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "playerdropdf.to_csv('D://PycharmProjects/TerraBot/data/faction-picker-bot/unfinishedgames.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# for coming back to edit\n",
+    "playerdropdf = pd.read_csv('D://PycharmProjects/TerraBot/data/faction-picker-bot/unfinishedgames.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Remove all the games which end up with 0 or 1 player."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>game</th>\n",
+       "      <th>nodrops</th>\n",
+       "      <th>startplayers</th>\n",
+       "      <th>playersdropped</th>\n",
+       "      <th>endplayers</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0000000001</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>050615</td>\n",
+       "      <td>True</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0512</td>\n",
+       "      <td>True</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>051501</td>\n",
+       "      <td>True</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>060303</td>\n",
+       "      <td>True</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115523</th>\n",
+       "      <td>115523</td>\n",
+       "      <td>Youreacrookcaptianhook</td>\n",
+       "      <td>False</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115524</th>\n",
+       "      <td>115524</td>\n",
+       "      <td>YourMomIsAChaosMagician</td>\n",
+       "      <td>True</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115525</th>\n",
+       "      <td>115525</td>\n",
+       "      <td>ytuwertqwtr</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115526</th>\n",
+       "      <td>115526</td>\n",
+       "      <td>yuertyqert</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115527</th>\n",
+       "      <td>115527</td>\n",
+       "      <td>ZeicheMasZeuchnis</td>\n",
+       "      <td>True</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>115528 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Unnamed: 0                     game  nodrops  startplayers  \\\n",
+       "0                0               0000000001     True             2   \n",
+       "1                1                   050615     True             4   \n",
+       "2                2                     0512     True             4   \n",
+       "3                3                   051501     True             5   \n",
+       "4                4                   060303     True             4   \n",
+       "...            ...                      ...      ...           ...   \n",
+       "115523      115523   Youreacrookcaptianhook    False             3   \n",
+       "115524      115524  YourMomIsAChaosMagician     True             3   \n",
+       "115525      115525              ytuwertqwtr     True             2   \n",
+       "115526      115526               yuertyqert     True             2   \n",
+       "115527      115527        ZeicheMasZeuchnis     True             3   \n",
+       "\n",
+       "        playersdropped  endplayers  \n",
+       "0                    0           2  \n",
+       "1                    0           4  \n",
+       "2                    0           4  \n",
+       "3                    0           5  \n",
+       "4                    0           4  \n",
+       "...                ...         ...  \n",
+       "115523               1           2  \n",
+       "115524               0           3  \n",
+       "115525               0           2  \n",
+       "115526               0           2  \n",
+       "115527               0           3  \n",
+       "\n",
+       "[115528 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "playerdropdf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "game events before: 73332500, game events after: 72827570, game events removed: 504930\n",
+      "games before: 115529, games after: 112739, games removed: 2790\n",
+      "gameST before: 693162, gameST after: 676434, games removed: 16728\n"
+     ]
+    }
+   ],
+   "source": [
+    "badgames = playerdropdf[playerdropdf['endplayers'].isin([0, 1])]\n",
+    "data = filteringByBadgames(data, badgames)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2790\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(len(playerdropdf[playerdropdf['endplayers'].isin([0, 1])]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Removing unwanted maps\n",
+    "We want these maps:  \n",
+    "126fe960806d587c78546b30f1a90853b1ada468 - map1  \n",
+    "95a66999127893f5925a5f591d54f8bcb9a670e6 - map2  \n",
+    "be8f6ebf549404d015547152d5f2a1906ae8dd90 - map3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "game events before: 72827570, game events after: 72067832, game events removed: 759738\n",
+      "games before: 112739, games after: 111547, games removed: 1192\n",
+      "gameST before: 676434, gameST after: 669282, games removed: 7152\n"
+     ]
+    }
+   ],
+   "source": [
+    "acceptablemaps = ['126fe960806d587c78546b30f1a90853b1ada468',   \n",
+    "                  '95a66999127893f5925a5f591d54f8bcb9a670e6', \n",
+    "                  'be8f6ebf549404d015547152d5f2a1906ae8dd90']\n",
+    "\n",
+    "badgames = games[~games[\"base_map\"].isin(acceptablemaps)]\n",
+    "data = filteringByBadgames(data, badgames)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating the full dataset <a name=\"fullDataset\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Progressed to 10000th game\n",
+      "Progressed to 20000th game\n",
+      "Progressed to 30000th game\n",
+      "Progressed to 40000th game\n",
+      "Progressed to 50000th game\n",
+      "Progressed to 60000th game\n",
+      "Progressed to 70000th game\n",
+      "Progressed to 80000th game\n",
+      "Progressed to 90000th game\n",
+      "Progressed to 100000th game\n",
+      "Progressed to 110000th game\n",
+      "no of unique games in table is: 111546\n"
+     ]
+    }
+   ],
+   "source": [
+    "vpdf, _, _ = makenewdf()\n",
+    "featdf, _ = emptyfeaturesdf()\n",
+    "\n",
+    "gameevents = data['gameevents']\n",
+    "games = data['games']\n",
+    "gamescoringtiles = data['gamescoringtiles']\n",
+    "\n",
+    "gameslist = list(pd.unique(gameevents['game']))\n",
+    "gamelengthlen = len(gameslist)\n",
+    "gamesroundup = math.ceil(gamelengthlen / 100.0) * 100\n",
+    "jj = 0\n",
+    "\n",
+    "for ii in range(100, gamesroundup+1, 100):     \n",
+    "    ii = min(ii, gamelengthlen)    \n",
+    "    if (ii % 10000) == 0:  # update every 10000 games\n",
+    "        print(f'Progressed to {ii}th game')\n",
+    "    \n",
+    "    next100games = gameslist[jj:ii]\n",
+    "    jj = ii  # so that we don't get any repetitions at the very end, where our set will be smaller\n",
+    "    \n",
+    "    gameevents100 = gameevents[gameevents['game'].isin(next100games)]\n",
+    "    gamemeta100 = games[games['game'].isin(next100games)]\n",
+    "    gameST100 = gamescoringtiles[gamescoringtiles['game'].isin(next100games)]\n",
+    "    endplayers100 = playerdropdf[playerdropdf['game'].isin(next100games)]  # use this for player count\n",
+    "\n",
+    "    for game in next100games:\n",
+    "        singlegame = gameevents100[gameevents100['game'] == game]\n",
+    "        singlegamemeta = gamemeta100[gamemeta100['game'] == game]\n",
+    "        singlegameST = gameST100[gameST100['game'] == game]\n",
+    "        singleendplayers = endplayers100[endplayers100['game'] == game]\n",
+    "        \n",
+    "        if not len(singlegame) == 0:\n",
+    "            vpforgame = get_vp_from_game(singlegame)\n",
+    "            featsforgame = get_features_from_game(singlegame, singlegamemeta, singlegameST, singleendplayers=singleendplayers)\n",
+    "            \n",
+    "            vpdf = vpdf.append(vpforgame, ignore_index=True)\n",
+    "            featdf = featdf.append(featsforgame, ignore_index=True)\n",
+    "\n",
+    "print(f\"no of unique games in table is: {len(list(pd.unique(vpdf['game'])))}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>game</th>\n",
+       "      <th>witches</th>\n",
+       "      <th>auren</th>\n",
+       "      <th>swarmlings</th>\n",
+       "      <th>mermaids</th>\n",
+       "      <th>cultists</th>\n",
+       "      <th>halflings</th>\n",
+       "      <th>dwarves</th>\n",
+       "      <th>engineers</th>\n",
+       "      <th>chaosmagicians</th>\n",
+       "      <th>giants</th>\n",
+       "      <th>fakirs</th>\n",
+       "      <th>nomads</th>\n",
+       "      <th>darklings</th>\n",
+       "      <th>alchemists</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0000000001</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>118.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>62.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>050615</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0512</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>124.0</td>\n",
+       "      <td>101.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>051501</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>84.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>78.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>060303</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>108.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>121.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>78.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111541</th>\n",
+       "      <td>Youreacrookcaptianhook</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>126.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111542</th>\n",
+       "      <td>YourMomIsAChaosMagician</td>\n",
+       "      <td>112.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>114.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111543</th>\n",
+       "      <td>ytuwertqwtr</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>102.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111544</th>\n",
+       "      <td>yuertyqert</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>141.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111545</th>\n",
+       "      <td>ZeicheMasZeuchnis</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>132.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>88.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>111546 rows × 15 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                           game  witches  auren  swarmlings  mermaids  \\\n",
+       "0                    0000000001      NaN    NaN         NaN     118.0   \n",
+       "1                        050615      NaN    NaN         NaN       NaN   \n",
+       "2                          0512      NaN    NaN         NaN       NaN   \n",
+       "3                        051501      NaN   84.0         NaN       NaN   \n",
+       "4                        060303      NaN    NaN         NaN     108.0   \n",
+       "...                         ...      ...    ...         ...       ...   \n",
+       "111541   Youreacrookcaptianhook      NaN    NaN       126.0       NaN   \n",
+       "111542  YourMomIsAChaosMagician    112.0    NaN       114.0       NaN   \n",
+       "111543              ytuwertqwtr      NaN    NaN       105.0       NaN   \n",
+       "111544               yuertyqert    128.0    NaN         NaN       NaN   \n",
+       "111545        ZeicheMasZeuchnis      NaN    NaN         NaN       NaN   \n",
+       "\n",
+       "        cultists  halflings  dwarves  engineers  chaosmagicians  giants  \\\n",
+       "0            NaN       62.0      NaN        NaN             NaN     NaN   \n",
+       "1            NaN        NaN      NaN        NaN             NaN     NaN   \n",
+       "2            NaN        NaN      NaN      124.0           101.0     NaN   \n",
+       "3            NaN       77.0      NaN        NaN            72.0     NaN   \n",
+       "4            NaN        NaN      NaN      121.0             NaN     NaN   \n",
+       "...          ...        ...      ...        ...             ...     ...   \n",
+       "111541       NaN      105.0      NaN        NaN             NaN     NaN   \n",
+       "111542       NaN        NaN      NaN        NaN             NaN     NaN   \n",
+       "111543       NaN        NaN    102.0        NaN             NaN     NaN   \n",
+       "111544       NaN        NaN      NaN        NaN           141.0     NaN   \n",
+       "111545       NaN        NaN    132.0        NaN             NaN     NaN   \n",
+       "\n",
+       "        fakirs  nomads  darklings  alchemists  \n",
+       "0          NaN     NaN        NaN         NaN  \n",
+       "1          NaN     NaN      122.0         NaN  \n",
+       "2          NaN     NaN        NaN         NaN  \n",
+       "3          NaN    54.0       78.0         NaN  \n",
+       "4         78.0     NaN      128.0         NaN  \n",
+       "...        ...     ...        ...         ...  \n",
+       "111541     NaN    37.0        NaN         NaN  \n",
+       "111542     NaN   120.0        NaN         NaN  \n",
+       "111543     NaN     NaN        NaN         NaN  \n",
+       "111544     NaN     NaN        NaN         NaN  \n",
+       "111545     NaN    88.0      120.0         NaN  \n",
+       "\n",
+       "[111546 rows x 15 columns]"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vpdf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
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+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>game</th>\n",
+       "      <th>r1_SCORE1</th>\n",
+       "      <th>r1_SCORE2</th>\n",
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+       "      <th>r1_SCORE4</th>\n",
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+       "      <th>...</th>\n",
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+       "      <th>3</th>\n",
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+       "    <tr>\n",
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+       "      <th>111541</th>\n",
+       "      <td>Youreacrookcaptianhook</td>\n",
+       "      <td>0</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111542</th>\n",
+       "      <td>YourMomIsAChaosMagician</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111545</th>\n",
+       "      <td>ZeicheMasZeuchnis</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>111546 rows × 72 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                           game r1_SCORE1 r1_SCORE2 r1_SCORE3 r1_SCORE4  \\\n",
+       "0                    0000000001         0         0         0         0   \n",
+       "1                        050615         0         0         0         0   \n",
+       "2                          0512         0         0         0         0   \n",
+       "3                        051501         0         0         0         0   \n",
+       "4                        060303         0         1         0         0   \n",
+       "...                         ...       ...       ...       ...       ...   \n",
+       "111541   Youreacrookcaptianhook         0         0         0         0   \n",
+       "111542  YourMomIsAChaosMagician         0         0         1         0   \n",
+       "111543              ytuwertqwtr         1         0         0         0   \n",
+       "111544               yuertyqert         0         0         0         1   \n",
+       "111545        ZeicheMasZeuchnis         0         0         1         0   \n",
+       "\n",
+       "       r1_SCORE5 r1_SCORE6 r1_SCORE7 r1_SCORE8 r1_SCORE9  ... BON8 BON9 BON10  \\\n",
+       "0              0         0         0         1         0  ...    0    1     1   \n",
+       "1              1         0         0         0         0  ...    1    1     0   \n",
+       "2              1         0         0         0         0  ...    1    1     0   \n",
+       "3              0         0         0         1         0  ...    1    0     0   \n",
+       "4              0         0         0         0         0  ...    1    1     1   \n",
+       "...          ...       ...       ...       ...       ...  ...  ...  ...   ...   \n",
+       "111541         0         1         0         0         0  ...    0    1     0   \n",
+       "111542         0         0         0         0         0  ...    1    1     0   \n",
+       "111543         0         0         0         0         0  ...    1    1     1   \n",
+       "111544         0         0         0         0         0  ...    1    0     1   \n",
+       "111545         0         0         0         0         0  ...    1    1     0   \n",
+       "\n",
+       "       2players 3players 4players 5players map1 map2 map3  \n",
+       "0             1        0        0        0    1    0    0  \n",
+       "1             0        0        1        0    0    0    1  \n",
+       "2             0        0        1        0    1    0    0  \n",
+       "3             0        0        0        1    1    0    0  \n",
+       "4             0        0        1        0    0    1    0  \n",
+       "...         ...      ...      ...      ...  ...  ...  ...  \n",
+       "111541        1        0        0        0    1    0    0  \n",
+       "111542        0        1        0        0    1    0    0  \n",
+       "111543        1        0        0        0    1    0    0  \n",
+       "111544        1        0        0        0    1    0    0  \n",
+       "111545        0        1        0        0    1    0    0  \n",
+       "\n",
+       "[111546 rows x 72 columns]"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "featdf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vpdf.to_csv('D://PycharmProjects/TerraBot/data/faction-picker-bot/vpdata.csv')\n",
+    "featdf.to_csv('D://PycharmProjects/TerraBot/data/faction-picker-bot/featdata.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# check we still have the same games:\n",
+    "gamesymmetricdif = set(vpdf['game']) ^ set(featdf['game'])\n",
+    "len(gamesymmetricdif)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/faction-picker-bot/.ipynb_checkpoints/creatingVPdata-diffeats-checkpoint.ipynb b/faction-picker-bot/.ipynb_checkpoints/creatingVPdata-diffeats-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..3a810f7568830e005e44223b2665fb11e05e0692
--- /dev/null
+++ b/faction-picker-bot/.ipynb_checkpoints/creatingVPdata-diffeats-checkpoint.ipynb
@@ -0,0 +1,4107 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import math\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Table of contents\n",
+    "1. [Loading data from the website](#websiteData)\n",
+    "2. [Loading data locally](#localData)\n",
+    "3. [Dataset creation initial functions](#datasetCreate)  \n",
+    "    a. [Victory Point (target) dataset](#VPData)  \n",
+    "    b. [Features dataset](#featuresData)\n",
+    "4. [Speeding check & speed up dataset creation & sense-checking on small batch](#speedUp)\n",
+    "5. [Cleaning & filtering the data](#cleanUp)\n",
+    "6. [Creating the full dataset](#fullDataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading data from the website (not using this right now - for now we'll just load locally) <a name=\"websiteData\"></a>\n",
+    "But we'll keep this section here for now, commented out. The reason we're not using this is because the jsons are nested, which would require a bit of time to unpack. I'd like to focus on the cleaning / making some form of useful data and modelling, for now. Later we can go back and get more data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# bigoldlist = ['2013-02.json',\n",
+    "# '2013-03.json',\n",
+    "# '2013-04.json',\n",
+    "# '2013-05.json',\n",
+    "# '2013-06.json',\n",
+    "# '2013-07.json',\n",
+    "# '2013-08.json',\n",
+    "# '2013-09.json',\n",
+    "# '2013-10.json',\n",
+    "# '2013-11.json',\n",
+    "# '2013-12.json',\n",
+    "# '2014-01.json',\n",
+    "# '2014-02.json',\n",
+    "# '2014-03.json',\n",
+    "# '2014-04.json',\n",
+    "# '2014-05.json',\n",
+    "# '2014-06.json',\n",
+    "# '2014-07.json',\n",
+    "# '2014-08.json',\n",
+    "# '2014-09.json',\n",
+    "# '2014-10.json',\n",
+    "# '2014-11.json',\n",
+    "# '2014-12.json',\n",
+    "# '2015-01.json',\n",
+    "# '2015-02.json',\n",
+    "# '2015-03.json',\n",
+    "# '2015-04.json',\n",
+    "# '2015-05.json',\n",
+    "# '2015-06.json',\n",
+    "# '2015-07.json',\n",
+    "# '2015-08.json',\n",
+    "# '2015-09.json',\n",
+    "# '2015-10.json',\n",
+    "# '2015-11.json',\n",
+    "# '2015-12.json',\n",
+    "# '2016-01.json',\n",
+    "# '2016-02.json',\n",
+    "# '2016-03.json',\n",
+    "# '2016-04.json',\n",
+    "# '2016-05.json',\n",
+    "# '2016-06.json',\n",
+    "# '2016-07.json',\n",
+    "# '2016-08.json',\n",
+    "# '2016-09.json',\n",
+    "# '2016-10.json',\n",
+    "# '2016-11.json',\n",
+    "# '2016-12.json',\n",
+    "# '2017-01.json',\n",
+    "# '2017-02.json',\n",
+    "# '2017-03.json',\n",
+    "# '2017-04.json',\n",
+    "# '2017-05.json',\n",
+    "# '2017-06.json',\n",
+    "# '2017-07.json',\n",
+    "# '2017-08.json',\n",
+    "# '2017-09.json',\n",
+    "# '2017-10.json',\n",
+    "# '2017-11.json',\n",
+    "# '2017-12.json',\n",
+    "# '2018-01.json',\n",
+    "# '2018-02.json',\n",
+    "# '2018-03.json',\n",
+    "# '2018-04.json',\n",
+    "# '2018-05.json',\n",
+    "# '2018-06.json',\n",
+    "# '2018-07.json',\n",
+    "# '2018-08.json',\n",
+    "# '2018-09.json',\n",
+    "# '2018-10.json',\n",
+    "# '2018-11.json',\n",
+    "# '2018-12.json',\n",
+    "# '2019-01.json',\n",
+    "# '2019-02.json',\n",
+    "# '2019-03.json',\n",
+    "# '2019-04.json',\n",
+    "# '2019-05.json',\n",
+    "# '2019-06.json',\n",
+    "# '2019-07.json',\n",
+    "# '2019-08.json',\n",
+    "# '2019-09.json',\n",
+    "# '2019-10.json',\n",
+    "# '2019-11.json',\n",
+    "# '2019-12.json',\n",
+    "# '2020-01.json',\n",
+    "# '2020-02.json',\n",
+    "# '2020-03.json',\n",
+    "# '2020-04.json',\n",
+    "# '2020-05.json',\n",
+    "# '2020-06.json',\n",
+    "# '2020-07.json',\n",
+    "# '2020-08.json',\n",
+    "# '2020-09.json',\n",
+    "# '2020-10.json',\n",
+    "# '2020-11.json',\n",
+    "# '2020-12.json',\n",
+    "# '2021-01.json',\n",
+    "# '2021-02.json',\n",
+    "# '2021-03.json',\n",
+    "# '2021-04.json']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # Test start of web pipeline (not used)\n",
+    "# url = 'https://terra.snellman.net/data/events/' + bigoldlist[0]\n",
+    "# eventsdata1 = pd.read_json(url)\n",
+    "# print(len(eventsdata1))\n",
+    "# eventsdata1.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # Load in the data remotely\n",
+    "# for gameset in bigoldlist:\n",
+    "#     url = 'https://terra.snellman.net/data/events/' + gameset\n",
+    "#     eventsdata = pd.read_json(url)\n",
+    "    \n",
+    "#     for eachgame in range(len(gameset)):\n",
+    "#         singlerow = eventsdata.iloc[[eachgame]]\n",
+    "#         newcol = get_vp_from_game(gameEventRow)\n",
+    "#         vpdf = vpdf.append(newdf, ignore_index=True)      "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading in the data Locally <a name=\"localData\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "folderlocation = \"D:/PycharmProjects/TerraBot/terra-mystica\"\n",
+    "gameevents = pd.read_csv(f'{folderlocation}/game_events.csv')\n",
+    "games = pd.read_csv(f'{folderlocation}/games.csv')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## EDA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of game events: 73419074\n",
+      "Number of games in gameevents: 115612\n",
+      "len of games: 115612\n"
+     ]
+    }
+   ],
+   "source": [
+    "# get some more stats\n",
+    "gameslist = list(pd.unique(gameevents['game']))\n",
+    "print(f'Number of game events: {len(gameevents)}')\n",
+    "print(f\"Number of games in gameevents: {len(gameslist)}\")\n",
+    "print(f'len of games: {len(games)}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>base_map</th>\n",
+       "      <th>game</th>\n",
+       "      <th>last_update</th>\n",
+       "      <th>player_count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>126fe960806d587c78546b30f1a90853b1ada468</td>\n",
+       "      <td>0000000001</td>\n",
+       "      <td>2015-07-22 05:15:51</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>be8f6ebf549404d015547152d5f2a1906ae8dd90</td>\n",
+       "      <td>050615</td>\n",
+       "      <td>2015-07-13 09:45:00</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>126fe960806d587c78546b30f1a90853b1ada468</td>\n",
+       "      <td>0512</td>\n",
+       "      <td>2015-07-03 04:17:31</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>126fe960806d587c78546b30f1a90853b1ada468</td>\n",
+       "      <td>051501</td>\n",
+       "      <td>2015-07-16 21:00:54</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>95a66999127893f5925a5f591d54f8bcb9a670e6</td>\n",
+       "      <td>060303</td>\n",
+       "      <td>2015-07-02 05:20:19</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115607</th>\n",
+       "      <td>126fe960806d587c78546b30f1a90853b1ada468</td>\n",
+       "      <td>Youreacrookcaptianhook</td>\n",
+       "      <td>2014-09-04 05:04:45</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115608</th>\n",
+       "      <td>126fe960806d587c78546b30f1a90853b1ada468</td>\n",
+       "      <td>YourMomIsAChaosMagician</td>\n",
+       "      <td>2014-09-02 00:23:43</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115609</th>\n",
+       "      <td>126fe960806d587c78546b30f1a90853b1ada468</td>\n",
+       "      <td>ytuwertqwtr</td>\n",
+       "      <td>2014-09-28 18:12:35</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115610</th>\n",
+       "      <td>126fe960806d587c78546b30f1a90853b1ada468</td>\n",
+       "      <td>yuertyqert</td>\n",
+       "      <td>2014-09-27 19:01:17</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115611</th>\n",
+       "      <td>126fe960806d587c78546b30f1a90853b1ada468</td>\n",
+       "      <td>ZeicheMasZeuchnis</td>\n",
+       "      <td>2014-09-10 20:35:32</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>115612 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                        base_map                     game  \\\n",
+       "0       126fe960806d587c78546b30f1a90853b1ada468               0000000001   \n",
+       "1       be8f6ebf549404d015547152d5f2a1906ae8dd90                   050615   \n",
+       "2       126fe960806d587c78546b30f1a90853b1ada468                     0512   \n",
+       "3       126fe960806d587c78546b30f1a90853b1ada468                   051501   \n",
+       "4       95a66999127893f5925a5f591d54f8bcb9a670e6                   060303   \n",
+       "...                                          ...                      ...   \n",
+       "115607  126fe960806d587c78546b30f1a90853b1ada468   Youreacrookcaptianhook   \n",
+       "115608  126fe960806d587c78546b30f1a90853b1ada468  YourMomIsAChaosMagician   \n",
+       "115609  126fe960806d587c78546b30f1a90853b1ada468              ytuwertqwtr   \n",
+       "115610  126fe960806d587c78546b30f1a90853b1ada468               yuertyqert   \n",
+       "115611  126fe960806d587c78546b30f1a90853b1ada468        ZeicheMasZeuchnis   \n",
+       "\n",
+       "                last_update  player_count  \n",
+       "0       2015-07-22 05:15:51             2  \n",
+       "1       2015-07-13 09:45:00             4  \n",
+       "2       2015-07-03 04:17:31             4  \n",
+       "3       2015-07-16 21:00:54             5  \n",
+       "4       2015-07-02 05:20:19             4  \n",
+       "...                     ...           ...  \n",
+       "115607  2014-09-04 05:04:45             3  \n",
+       "115608  2014-09-02 00:23:43             4  \n",
+       "115609  2014-09-28 18:12:35             2  \n",
+       "115610  2014-09-27 19:01:17             2  \n",
+       "115611  2014-09-10 20:35:32             3  \n",
+       "\n",
+       "[115612 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "games"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>event</th>\n",
+       "      <th>faction</th>\n",
+       "      <th>game</th>\n",
+       "      <th>num</th>\n",
+       "      <th>round</th>\n",
+       "      <th>turn</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>upgrade:SH</td>\n",
+       "      <td>halflings</td>\n",
+       "      <td>0000000001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>order:2</td>\n",
+       "      <td>halflings</td>\n",
+       "      <td>0000000001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>order:2</td>\n",
+       "      <td>halflings</td>\n",
+       "      <td>0000000001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>order:1</td>\n",
+       "      <td>halflings</td>\n",
+       "      <td>0000000001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>order:1</td>\n",
+       "      <td>halflings</td>\n",
+       "      <td>0000000001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73419069</th>\n",
+       "      <td>burn</td>\n",
+       "      <td>darklings</td>\n",
+       "      <td>ZeicheMasZeuchnis</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73419070</th>\n",
+       "      <td>burn</td>\n",
+       "      <td>darklings</td>\n",
+       "      <td>ZeicheMasZeuchnis</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73419071</th>\n",
+       "      <td>favor:FAV10</td>\n",
+       "      <td>darklings</td>\n",
+       "      <td>ZeicheMasZeuchnis</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73419072</th>\n",
+       "      <td>town:TW3</td>\n",
+       "      <td>darklings</td>\n",
+       "      <td>ZeicheMasZeuchnis</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73419073</th>\n",
+       "      <td>action:ACT3</td>\n",
+       "      <td>darklings</td>\n",
+       "      <td>ZeicheMasZeuchnis</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>73419074 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                event    faction               game  num  round  turn\n",
+       "0          upgrade:SH  halflings         0000000001    1      2     1\n",
+       "1             order:2  halflings         0000000001    1      6     1\n",
+       "2             order:2  halflings         0000000001    1      2     1\n",
+       "3             order:1  halflings         0000000001    1      1     1\n",
+       "4             order:1  halflings         0000000001    1      5     1\n",
+       "...               ...        ...                ...  ...    ...   ...\n",
+       "73419069         burn  darklings  ZeicheMasZeuchnis    4      1     1\n",
+       "73419070         burn  darklings  ZeicheMasZeuchnis    3      5     2\n",
+       "73419071  favor:FAV10  darklings  ZeicheMasZeuchnis    1      3     3\n",
+       "73419072     town:TW3  darklings  ZeicheMasZeuchnis    1      6     2\n",
+       "73419073  action:ACT3  darklings  ZeicheMasZeuchnis    1      1     1\n",
+       "\n",
+       "[73419074 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gameevents"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['halflings', 'all', 'mermaids', 'dragonlords', 'riverwalkers',\n",
+       "       'yetis', 'darklings', 'chaosmagicians', 'engineers', 'icemaidens',\n",
+       "       'nomads', 'auren', 'fakirs', 'witches', 'cultists', 'alchemists',\n",
+       "       'swarmlings', 'dwarves', 'shapeshifters', 'nofaction1',\n",
+       "       'nofaction4', 'acolytes', 'giants', 'nofaction3', 'nofaction2',\n",
+       "       'nofaction5', 'nofaction7'], dtype=object)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "allfactions = pd.unique(gameevents['faction'])\n",
+    "allfactions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gamescoringtiles = pd.read_csv(f'{folderlocation}/game_scoring_tiles.csv')\n",
+    "gameoptions = pd.read_csv(f'{folderlocation}/game_options.csv')\n",
+    "stats = pd.read_csv(f'{folderlocation}/stats.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>game</th>\n",
+       "      <th>round</th>\n",
+       "      <th>tile</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0000000001</td>\n",
+       "      <td>3</td>\n",
+       "      <td>SCORE1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0000000001</td>\n",
+       "      <td>6</td>\n",
+       "      <td>SCORE5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0000000001</td>\n",
+       "      <td>5</td>\n",
+       "      <td>SCORE4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0000000001</td>\n",
+       "      <td>1</td>\n",
+       "      <td>SCORE8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0000000001</td>\n",
+       "      <td>2</td>\n",
+       "      <td>SCORE3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>693655</th>\n",
+       "      <td>ZeicheMasZeuchnis</td>\n",
+       "      <td>4</td>\n",
+       "      <td>SCORE1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>693656</th>\n",
+       "      <td>ZeicheMasZeuchnis</td>\n",
+       "      <td>6</td>\n",
+       "      <td>SCORE5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>693657</th>\n",
+       "      <td>ZeicheMasZeuchnis</td>\n",
+       "      <td>5</td>\n",
+       "      <td>SCORE7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>693658</th>\n",
+       "      <td>ZeicheMasZeuchnis</td>\n",
+       "      <td>1</td>\n",
+       "      <td>SCORE3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>693659</th>\n",
+       "      <td>ZeicheMasZeuchnis</td>\n",
+       "      <td>2</td>\n",
+       "      <td>SCORE8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>693660 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     game  round    tile\n",
+       "0              0000000001      3  SCORE1\n",
+       "1              0000000001      6  SCORE5\n",
+       "2              0000000001      5  SCORE4\n",
+       "3              0000000001      1  SCORE8\n",
+       "4              0000000001      2  SCORE3\n",
+       "...                   ...    ...     ...\n",
+       "693655  ZeicheMasZeuchnis      4  SCORE1\n",
+       "693656  ZeicheMasZeuchnis      6  SCORE5\n",
+       "693657  ZeicheMasZeuchnis      5  SCORE7\n",
+       "693658  ZeicheMasZeuchnis      1  SCORE3\n",
+       "693659  ZeicheMasZeuchnis      2  SCORE8\n",
+       "\n",
+       "[693660 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gamescoringtiles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SCORE3    83727\n",
+       "SCORE7    83435\n",
+       "SCORE8    83322\n",
+       "SCORE4    83270\n",
+       "SCORE2    83226\n",
+       "SCORE5    83188\n",
+       "SCORE6    82770\n",
+       "SCORE1    71728\n",
+       "SCORE9    38994\n",
+       "Name: tile, dtype: int64"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gamescoringtiles['tile'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "19\n",
+      "option-errata-cultist-power                 0.106882\n",
+      "option-strict-leech                         0.106427\n",
+      "option-strict-darkling-sh                   0.103665\n",
+      "option-strict-chaosmagician-sh              0.103642\n",
+      "option-email-notify                         0.100557\n",
+      "option-mini-expansion-1                     0.089982\n",
+      "option-shipping-bonus                       0.083785\n",
+      "option-variable-turn-order                  0.075660\n",
+      "option-temple-scoring-tile                  0.053650\n",
+      "option-fire-and-ice-factions/ice            0.039872\n",
+      "option-fire-and-ice-final-scoring           0.039481\n",
+      "option-fire-and-ice-factions/volcano        0.039336\n",
+      "option-fire-and-ice-factions/variable_v5    0.024844\n",
+      "option-maintain-player-order                0.021085\n",
+      "option-fire-and-ice-factions/variable_v3    0.004580\n",
+      "option-fire-and-ice-factions/variable       0.003220\n",
+      "option-fire-and-ice-factions/variable_v4    0.002017\n",
+      "option-fire-and-ice-factions/variable_v2    0.001303\n",
+      "option-loose-adjust-resource                0.000012\n",
+      "Name: option, dtype: float64\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>game</th>\n",
+       "      <th>option</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0000000001</td>\n",
+       "      <td>option-strict-darkling-sh</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0000000001</td>\n",
+       "      <td>option-email-notify</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0000000001</td>\n",
+       "      <td>option-shipping-bonus</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0000000001</td>\n",
+       "      <td>option-strict-chaosmagician-sh</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0000000001</td>\n",
+       "      <td>option-errata-cultist-power</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1075569</th>\n",
+       "      <td>ZeicheMasZeuchnis</td>\n",
+       "      <td>option-email-notify</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1075570</th>\n",
+       "      <td>ZeicheMasZeuchnis</td>\n",
+       "      <td>option-strict-chaosmagician-sh</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1075571</th>\n",
+       "      <td>ZeicheMasZeuchnis</td>\n",
+       "      <td>option-errata-cultist-power</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1075572</th>\n",
+       "      <td>ZeicheMasZeuchnis</td>\n",
+       "      <td>option-mini-expansion-1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1075573</th>\n",
+       "      <td>ZeicheMasZeuchnis</td>\n",
+       "      <td>option-strict-leech</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1075574 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      game                          option\n",
+       "0               0000000001       option-strict-darkling-sh\n",
+       "1               0000000001             option-email-notify\n",
+       "2               0000000001           option-shipping-bonus\n",
+       "3               0000000001  option-strict-chaosmagician-sh\n",
+       "4               0000000001     option-errata-cultist-power\n",
+       "...                    ...                             ...\n",
+       "1075569  ZeicheMasZeuchnis             option-email-notify\n",
+       "1075570  ZeicheMasZeuchnis  option-strict-chaosmagician-sh\n",
+       "1075571  ZeicheMasZeuchnis     option-errata-cultist-power\n",
+       "1075572  ZeicheMasZeuchnis         option-mini-expansion-1\n",
+       "1075573  ZeicheMasZeuchnis             option-strict-leech\n",
+       "\n",
+       "[1075574 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(len(pd.unique(gameoptions['option'])))\n",
+    "print(gameoptions['option'].value_counts() / len(gameoptions))\n",
+    "gameoptions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "vp              12839907\n",
+       "build:D          6570095\n",
+       "leech:pw         5716489\n",
+       "leech:count      5716489\n",
+       "upgrade:TP       3543848\n",
+       "                  ...   \n",
+       "action:ACTH5        1852\n",
+       "order:6              912\n",
+       "order:7              636\n",
+       "action:ACTH4         592\n",
+       "action:ACTH3         314\n",
+       "Name: event, Length: 91, dtype: int64"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gameevents['event'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "126fe960806d587c78546b30f1a90853b1ada468    77158\n",
+       "95a66999127893f5925a5f591d54f8bcb9a670e6    21052\n",
+       "be8f6ebf549404d015547152d5f2a1906ae8dd90    16184\n",
+       "fdb13a13cd48b7a3c3525f27e4628ff6905aa5b1     1210\n",
+       "224736500d20520f195970eb0fd4c41df040c08c        3\n",
+       "735b073fd7161268bb2796c1275abda92acd8b1a        2\n",
+       "c07f36f9e050992d2daf6d44af2bc51dca719c46        1\n",
+       "30b6ded823e53670624981abdb2c5b8568a44091        1\n",
+       "b109f78907d2cbd5699ced16572be46043558e41        1\n",
+       "Name: base_map, dtype: int64"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "games['base_map'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[126fe960806d587c78546b30f1a90853b1ada468](https://terra.snellman.net/map/126fe960806d587c78546b30f1a90853b1ada468)\n",
+    "is the same as [91645cdb135773c2a7a50e5ca9cb18af54c664c4](https://terra.snellman.net/mapedit/91645cdb135773c2a7a50e5ca9cb18af54c664c4)  \n",
+    "[95a66999127893f5925a5f591d54f8bcb9a670e6](https://terra.snellman.net/map/95a66999127893f5925a5f591d54f8bcb9a670e6) is different  \n",
+    "[be8f6ebf549404d015547152d5f2a1906ae8dd90](https://terra.snellman.net/map/be8f6ebf549404d015547152d5f2a1906ae8dd90) is re-balanced.  \n",
+    "\n",
+    "All other maps aren't present here so we'll use those"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating the new datasets - some functions to help us make these later <a name=\"datasetCreate\"></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Victory Points (target) dataset <a name=\"VPData\"></a>\n",
+    "Let's make some functions to help make the dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def makenewdf():\n",
+    "    \"\"\"make an empty dataframe, organised in the way we want the target data, ready to be populated\"\"\"\n",
+    "    validfactions = ['witches', 'auren', 'swarmlings', 'mermaids', 'cultists', 'halflings', 'dwarves', 'engineers', 'chaosmagicians', 'giants', 'fakirs', 'nomads', 'darklings', 'alchemists']\n",
+    "    dfcols = ['game'] + validfactions\n",
+    "    vpdf = pd.DataFrame(columns=dfcols)\n",
+    "    \n",
+    "    return vpdf, dfcols, validfactions\n",
+    "\n",
+    "vpdf, dfcols, validfactions = makenewdf()\n",
+    "\n",
+    "sensecheck = False\n",
+    "if sensecheck:\n",
+    "    newdf = pd.DataFrame([[np.nan] * 15], columns=dfcols)\n",
+    "    newdf['auren'].replace({np.nan: 'test'}, inplace=True)\n",
+    "    vpdf = vpdf.append(newdf, ignore_index=True)\n",
+    "    print(len(vpdf))\n",
+    "    vpdf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_vp_from_game(singleGameEvents):\n",
+    "    \"\"\"Input game events for a single game. This is a pd.DataFrame. \n",
+    "    Output a row where each faction in the game has its vp populated (the rest are nans)\n",
+    "    \"\"\"\n",
+    "    newdf = pd.DataFrame([[np.nan] * 15], columns=dfcols)\n",
+    "    \n",
+    "    # assign the game number\n",
+    "    gameno = list(pd.unique(singleGameEvents['game']))\n",
+    "    \n",
+    "    # assert len(gameno) == 1, 'More than 1 unique game was found'    \n",
+    "    try:\n",
+    "        newdf['game'].replace({np.nan: gameno[0]}, inplace=True)\n",
+    "    except:\n",
+    "        print(f'DEBUGGING: len of table is {len(singleGameEvents)}')\n",
+    "        print(f'DEBUGGING: gamnos list: {gameno}')\n",
+    "        print(singleGameEvents)\n",
+    "        raise\n",
+    "        \n",
+    "    # find factions - there are some artifacts in the data. E.g. the \"faction\", \"all\". We need to filter them out.\n",
+    "    rawfactions = list(pd.unique(singleGameEvents['faction']))\n",
+    "    verifiedfactions = [rawfaction for rawfaction in rawfactions if rawfaction in validfactions]\n",
+    "    \n",
+    "    for faction in verifiedfactions:\n",
+    "        vpfaction = sum(singleGameEvents[(singleGameEvents['event'] == 'vp') & (singleGameEvents['faction'] == faction)]['num'])\n",
+    "        newdf[faction].replace({np.nan: vpfaction}, inplace=True)\n",
+    "        \n",
+    "    return newdf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time elapsed: 3.538278818130493s\n",
+      "Time to do all games at current speed: 113.62985853380627hrs\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>game</th>\n",
+       "      <th>witches</th>\n",
+       "      <th>auren</th>\n",
+       "      <th>swarmlings</th>\n",
+       "      <th>mermaids</th>\n",
+       "      <th>cultists</th>\n",
+       "      <th>halflings</th>\n",
+       "      <th>dwarves</th>\n",
+       "      <th>engineers</th>\n",
+       "      <th>chaosmagicians</th>\n",
+       "      <th>giants</th>\n",
+       "      <th>fakirs</th>\n",
+       "      <th>nomads</th>\n",
+       "      <th>darklings</th>\n",
+       "      <th>alchemists</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0000000001</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>118.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>62.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         game  witches  auren  swarmlings  mermaids  cultists  halflings  \\\n",
+       "0  0000000001      NaN    NaN         NaN     118.0       NaN       62.0   \n",
+       "\n",
+       "   dwarves  engineers  chaosmagicians  giants  fakirs  nomads  darklings  \\\n",
+       "0      NaN        NaN             NaN     NaN     NaN     NaN        NaN   \n",
+       "\n",
+       "   alchemists  \n",
+       "0         NaN  "
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# sense check\n",
+    "start = time.time()\n",
+    "singlegame = gameevents[gameevents['game'] == '0000000001']\n",
+    "singlegamefactions = (list(pd.unique(singlegame['faction'])))\n",
+    "vpforgame = get_vp_from_game(singlegame)\n",
+    "end = time.time()\n",
+    "\n",
+    "print(f'Time elapsed: {end-start}s')\n",
+    "print(f'Time to do all games at current speed: {((end-start)*len(pd.unique(gameevents[\"game\"]))/3600)}hrs')\n",
+    "vpforgame"
+   ]
+  },
+  {
+   "attachments": {
+    "a71ce2ba-dd59-49b1-aefc-8b6e1c14f8da.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That checks out (not inc the starting 20 vp)\n",
+    "\n",
+    "![image.png](attachment:a71ce2ba-dd59-49b1-aefc-8b6e1c14f8da.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Features dataset <a name=\"featuresData\"></a>\n",
+    "We'll want:\n",
+    "1. Boolean of bonus tiles (in game or not)  <span style=\"color:green\">- DONE</span>\n",
+    "2. One-hot of round tiles, for each round  <span style=\"color:green\">- DONE</span>\n",
+    "3. Boolean of factions already picked (more info if any have been picked yet) <span style=\"color:orange\">- removed as would require 1 row for each faction. We want 1 line per game for now</span>\n",
+    "4. One-hot for different map variants we'd like to use  <span style=\"color:green\">- DONE</span>\n",
+    "5. One-hot player count (from 2, 3, 4 or 5 players)  <span style=\"color:green\">- DONE</span>"
+   ]
+  },
+  {
+   "attachments": {
+    "942cd242-5b8b-432f-8843-51185679848e.png": {
+     "image/png": "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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These are the bonus tiles available:  \n",
+    "![image.png](attachment:942cd242-5b8b-432f-8843-51185679848e.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['pass:BON10', 'build:D', 'pass:BON3'], dtype=object)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.unique(gameevents[(gameevents['round'] == 0) & (gameevents['game'] == 'ytuwertqwtr')]['event'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But it looks like we only have details on the bonus tiles selected in the middle of the game. Where can we get information about all bonus tiles in game!? I'm not sure, so I'll use the only info that's available - which is the bonus tiles that players select. However, if there's a bonus tile that was never selected during a game, it won't be shown. If we go onto the Snellman website and re-watch an old game, we can see what bonus tiles are available in the GUI, so this info must be stored somewhere (as game history GUI must be using the same data we're looking at...?)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time elapsed: 3.645721197128296s\n",
+      "['BON8', 'BON6', 'BON3', 'BON10', 'BON9']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# working out the code to get the bonus tiles & timing this\n",
+    "start = time.time()\n",
+    "allevents = list(pd.unique(gameevents[gameevents['game'] == 'ytuwertqwtr']['event']))\n",
+    "bonustiles = [event[5:] for event in allevents if event.startswith('pass:BON')]\n",
+    "end = time.time()\n",
+    "print(f'Time elapsed: {end-start}s')\n",
+    "print(bonustiles)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['game', 'r1_SCORE1', 'r1_SCORE2', 'r1_SCORE3', 'r1_SCORE4', 'r1_SCORE5', 'r1_SCORE6', 'r1_SCORE7', 'r1_SCORE8', 'r1_SCORE9', 'r2_SCORE1', 'r2_SCORE2', 'r2_SCORE3', 'r2_SCORE4', 'r2_SCORE5', 'r2_SCORE6', 'r2_SCORE7', 'r2_SCORE8', 'r2_SCORE9', 'r3_SCORE1', 'r3_SCORE2', 'r3_SCORE3', 'r3_SCORE4', 'r3_SCORE5', 'r3_SCORE6', 'r3_SCORE7', 'r3_SCORE8', 'r3_SCORE9', 'r4_SCORE1', 'r4_SCORE2', 'r4_SCORE3', 'r4_SCORE4', 'r4_SCORE5', 'r4_SCORE6', 'r4_SCORE7', 'r4_SCORE8', 'r4_SCORE9', 'r5_SCORE1', 'r5_SCORE2', 'r5_SCORE3', 'r5_SCORE4', 'r5_SCORE5', 'r5_SCORE6', 'r5_SCORE7', 'r5_SCORE8', 'r5_SCORE9', 'r6_SCORE1', 'r6_SCORE2', 'r6_SCORE3', 'r6_SCORE4', 'r6_SCORE5', 'r6_SCORE6', 'r6_SCORE7', 'r6_SCORE8', 'r6_SCORE9', 'BON1', 'BON2', 'BON3', 'BON4', 'BON5', 'BON6', 'BON7', 'BON8', 'BON9', 'BON10', '2players', '3players', '4players', '5players', 'map1', 'map2', 'map3']\n",
+      "72\n"
+     ]
+    }
+   ],
+   "source": [
+    "def emptyfeaturesdf():\n",
+    "    \"\"\"make an empty dataframe, organised in the way we want the feature data, ready to be populated\"\"\"\n",
+    "    colnames = ['game']\n",
+    "    uniqueScoreTiles = np.sort(pd.unique(gamescoringtiles['tile']))\n",
+    "    \n",
+    "    # One-hot of round tiles, for each round\n",
+    "    for gameround in range(1, 7):\n",
+    "        roundstr = f'r{gameround}'\n",
+    "        for tile in uniqueScoreTiles:\n",
+    "            colnames.append(roundstr + '_' + tile)\n",
+    "    \n",
+    "    # Boolean of bonus tiles\n",
+    "    for bon in range(1, 11):\n",
+    "        colnames.append(f'BON{bon}')\n",
+    "        \n",
+    "    # One-hot player count (from 2, 3, 4 or 5 players)\n",
+    "    for player in range(2, 6):\n",
+    "        colnames.append(f'{player}players')\n",
+    "    \n",
+    "    # one hot of the map used\n",
+    "    \"\"\"126fe960806d587c78546b30f1a90853b1ada468 - map1\n",
+    "       95a66999127893f5925a5f591d54f8bcb9a670e6 - map2\n",
+    "       be8f6ebf549404d015547152d5f2a1906ae8dd90 - map3\n",
+    "    \"\"\"\n",
+    "    colnames = colnames + ['map1', 'map2', 'map3']\n",
+    "    \n",
+    "    featuresdf = pd.DataFrame(columns=colnames)\n",
+    "    \n",
+    "    return featuresdf, colnames\n",
+    "\n",
+    "featuresdf, featcolnames = emptyfeaturesdf()\n",
+    "print(featcolnames)\n",
+    "print(len(featcolnames))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_features_from_game(singlegameevents, singlegamemeta, singlegameST, singleendplayers=None):\n",
+    "    \"\"\"\n",
+    "    Inputs:\n",
+    "        singlegameevents <pd.DataFrame>   - is game events for a single game \n",
+    "        singlegamemeta   <pd.DataFrame>   - is a single row from `games` that gives map & player count\n",
+    "        singlegameST     <pd.DataFrame>   - is a single row from `gamescoringtiles` that gives... score tile (suprisingly)\n",
+    "        singleendplayers <pd.DataFrame>   - is a single row from `end players` that gives the amount of players at end of game, after dropouts\n",
+    "    Return:          <pd.DataFrame>   - a row where features have been found (will be sparse)\n",
+    "    \"\"\"\n",
+    "    newdf = pd.DataFrame([[0] * len(featcolnames)], columns=featcolnames)\n",
+    "    \n",
+    "    # assign game string\n",
+    "    singlegamemeta.iloc[0]['game']\n",
+    "    newdf['game'].replace({0: singlegamemeta.iloc[0]['game']}, inplace=True)\n",
+    "    \n",
+    "    # find the round tiles for each round\n",
+    "    for gameround in range(1, 7):\n",
+    "        roundstr = f'r{gameround}'\n",
+    "        scoretile = roundstr + '_' + singlegameST[singlegameST['round'] == gameround]['tile'].values[0]\n",
+    "        newdf[scoretile].replace({0: 1}, inplace=True)\n",
+    "        \n",
+    "    # Boolean of bonus tiles\n",
+    "    uniqueevents = list(pd.unique(singlegameevents['event']))\n",
+    "    bonustiles = [event[5:] for event in uniqueevents if event.startswith('pass:BON')]\n",
+    "    for bontile in bonustiles:\n",
+    "        newdf[bontile].replace({0: 1}, inplace=True)\n",
+    "       \n",
+    "    # One-hot player count (from 2, 3, 4 or 5 players)\n",
+    "    if singleendplayers is None:\n",
+    "        noplayers = singlegamemeta.iloc[0]['player_count']\n",
+    "        print('gamemeta used for player count')\n",
+    "    else:\n",
+    "        noplayers = singleendplayers.iloc[0]['endplayers']\n",
+    "        \n",
+    "    players = f'{noplayers}players'\n",
+    "    newdf[players].replace({0: 1}, inplace=True)\n",
+    "          \n",
+    "    # one hot of the map used\n",
+    "    mapdict = {'126fe960806d587c78546b30f1a90853b1ada468': 'map1',\n",
+    "               '95a66999127893f5925a5f591d54f8bcb9a670e6': 'map2',\n",
+    "               'be8f6ebf549404d015547152d5f2a1906ae8dd90': 'map3'\n",
+    "              }\n",
+    "    basemap = singlegamemeta.iloc[0]['base_map']\n",
+    "    gamemap = mapdict[basemap]\n",
+    "    newdf[gamemap].replace({0: 1}, inplace=True)\n",
+    "        \n",
+    "    return newdf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gamemeta used for player count\n",
+      "Time elapsed: 3.5182363986968994s\n",
+      "Time to do all games at current speed: 112.98620736837387hrs\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>game</th>\n",
+       "      <th>r1_SCORE1</th>\n",
+       "      <th>r1_SCORE2</th>\n",
+       "      <th>r1_SCORE3</th>\n",
+       "      <th>r1_SCORE4</th>\n",
+       "      <th>r1_SCORE5</th>\n",
+       "      <th>r1_SCORE6</th>\n",
+       "      <th>r1_SCORE7</th>\n",
+       "      <th>r1_SCORE8</th>\n",
+       "      <th>r1_SCORE9</th>\n",
+       "      <th>...</th>\n",
+       "      <th>BON8</th>\n",
+       "      <th>BON9</th>\n",
+       "      <th>BON10</th>\n",
+       "      <th>2players</th>\n",
+       "      <th>3players</th>\n",
+       "      <th>4players</th>\n",
+       "      <th>5players</th>\n",
+       "      <th>map1</th>\n",
+       "      <th>map2</th>\n",
+       "      <th>map3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0000000001</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 72 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         game  r1_SCORE1  r1_SCORE2  r1_SCORE3  r1_SCORE4  r1_SCORE5  \\\n",
+       "0  0000000001          0          0          0          0          0   \n",
+       "\n",
+       "   r1_SCORE6  r1_SCORE7  r1_SCORE8  r1_SCORE9  ...  BON8  BON9  BON10  \\\n",
+       "0          0          0          1          0  ...     0     1      1   \n",
+       "\n",
+       "   2players  3players  4players  5players  map1  map2  map3  \n",
+       "0         1         0         0         0     1     0     0  \n",
+       "\n",
+       "[1 rows x 72 columns]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# sense check\n",
+    "start = time.time()\n",
+    "singlegame = gameevents[gameevents['game'] == '0000000001']\n",
+    "singlegamemeta = games[games['game'] == '0000000001']\n",
+    "singlegameST = gamescoringtiles[gamescoringtiles['game'] == '0000000001']\n",
+    "vpforgame = get_vp_from_game(singlegame)\n",
+    "featsforgame = get_features_from_game(singlegame, singlegamemeta, singlegameST)\n",
+    "end = time.time()\n",
+    "\n",
+    "print(f'Time elapsed: {end-start}s')\n",
+    "print(f'Time to do all games at current speed: {((end-start)*len(pd.unique(gameevents[\"game\"]))/3600)}hrs')\n",
+    "featsforgame"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Speeding up dataset creation <a name=\"speedUp\"></a>\n",
+    "350 hours: that's way too long. How long will it take to split the table into 1500, 100 game tables and work on those?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time elapsed to make small table: 1.233781099319458s\n",
+      "gamemeta used for player count\n",
+      "Time elapsed to get data for single game now: 0.03003406524658203s\n",
+      "Time elapsed to do all small tables: 0.39622194570700325hrs\n",
+      "Time elapsed to do all data creation: 0.9645273198021783hrs\n",
+      "Time elapsed to do all processing: 1.3607492655091815hrs\n"
+     ]
+    }
+   ],
+   "source": [
+    "first100games = gameslist[:100]\n",
+    "\n",
+    "start = time.time()\n",
+    "gameevents100 = gameevents[gameevents['game'].isin(first100games)]\n",
+    "gamemeta100 = games[games['game'].isin(first100games)]\n",
+    "gameST100 = gamescoringtiles[gamescoringtiles['game'].isin(first100games)]\n",
+    "end = time.time()\n",
+    "smalltablestime = end-start\n",
+    "\n",
+    "print(f'Time elapsed to make small table: {smalltablestime}s')\n",
+    "\n",
+    "start = time.time()\n",
+    "singlegame = gameevents100[gameevents100['game'] == '0000000001']\n",
+    "singlegamemeta = gamemeta100[gamemeta100['game'] == '0000000001']\n",
+    "singlegameST = gameST100[gameST100['game'] == '0000000001']\n",
+    "vpforgame = get_vp_from_game(singlegame)\n",
+    "featsforgame = get_features_from_game(singlegame, singlegamemeta, singlegameST)\n",
+    "end = time.time()\n",
+    "datacreatetime = end-start\n",
+    "\n",
+    "print(f'Time elapsed to get data for single game now: {end-start}s')\n",
+    "totalsmalltablestime = smalltablestime * len(pd.unique(gameevents[\"game\"]))/(100 * 3600)  # divide by 100 as that's the size of table, divide by 3600 for s -> hrs\n",
+    "totaldatacreatetime = datacreatetime * len(pd.unique(gameevents[\"game\"]))/3600\n",
+    "print(f'Time elapsed to do all small tables: {totalsmalltablestime}hrs')\n",
+    "print(f'Time elapsed to do all data creation: {totaldatacreatetime}hrs')\n",
+    "print(f'Time elapsed to do all processing: {totalsmalltablestime + totaldatacreatetime}hrs')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Damn. I've just saved ~200 times the amount of time by doing that. As a complete aside, for pure interest, I wonder what the best trade-off between spitting into smaller & smaller tables (which increases time to do this) is vs saving time to then filter with smaller tables, is? (will check this out later)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Progressed to 100th game\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "Progressed to 200th game\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "gamemeta used for player count\n",
+      "no of unique games in table is: 200\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>game</th>\n",
+       "      <th>witches</th>\n",
+       "      <th>auren</th>\n",
+       "      <th>swarmlings</th>\n",
+       "      <th>mermaids</th>\n",
+       "      <th>cultists</th>\n",
+       "      <th>halflings</th>\n",
+       "      <th>dwarves</th>\n",
+       "      <th>engineers</th>\n",
+       "      <th>chaosmagicians</th>\n",
+       "      <th>giants</th>\n",
+       "      <th>fakirs</th>\n",
+       "      <th>nomads</th>\n",
+       "      <th>darklings</th>\n",
+       "      <th>alchemists</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0000000001</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>118.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>62.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>050615</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0512</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>124.0</td>\n",
+       "      <td>101.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>051501</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>84.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>78.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>060303</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>108.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>121.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>78.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>195</th>\n",
+       "      <td>4pLeague_S7_D6L17_G7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>73.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>130.0</td>\n",
+       "      <td>87.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>196</th>\n",
+       "      <td>4pLeague_S7_D6L18_G2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>108.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>197</th>\n",
+       "      <td>4pLeague_S7_D6L18_G5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>82.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>110.0</td>\n",
+       "      <td>117.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>198</th>\n",
+       "      <td>4pLeague_S7_D6L18_G6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>78.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>103.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>137.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>61.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>199</th>\n",
+       "      <td>4pLeague_S7_D6L18_G7</td>\n",
+       "      <td>95.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>200 rows × 15 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     game  witches  auren  swarmlings  mermaids  cultists  \\\n",
+       "0              0000000001      NaN    NaN         NaN     118.0       NaN   \n",
+       "1                  050615      NaN    NaN         NaN       NaN       NaN   \n",
+       "2                    0512      NaN    NaN         NaN       NaN       NaN   \n",
+       "3                  051501      NaN   84.0         NaN       NaN       NaN   \n",
+       "4                  060303      NaN    NaN         NaN     108.0       NaN   \n",
+       "..                    ...      ...    ...         ...       ...       ...   \n",
+       "195  4pLeague_S7_D6L17_G7      NaN    NaN        48.0       NaN       NaN   \n",
+       "196  4pLeague_S7_D6L18_G2      NaN  105.0         NaN       NaN       NaN   \n",
+       "197  4pLeague_S7_D6L18_G5      NaN    NaN        97.0       NaN       NaN   \n",
+       "198  4pLeague_S7_D6L18_G6      NaN    NaN        78.0       NaN       NaN   \n",
+       "199  4pLeague_S7_D6L18_G7     95.0    NaN         NaN       NaN       NaN   \n",
+       "\n",
+       "     halflings  dwarves  engineers  chaosmagicians  giants  fakirs  nomads  \\\n",
+       "0         62.0      NaN        NaN             NaN     NaN     NaN     NaN   \n",
+       "1          NaN      NaN        NaN             NaN     NaN     NaN     NaN   \n",
+       "2          NaN      NaN      124.0           101.0     NaN     NaN     NaN   \n",
+       "3         77.0      NaN        NaN            72.0     NaN     NaN    54.0   \n",
+       "4          NaN      NaN      121.0             NaN     NaN    78.0     NaN   \n",
+       "..         ...      ...        ...             ...     ...     ...     ...   \n",
+       "195        NaN      NaN        NaN            73.0     NaN     NaN   130.0   \n",
+       "196        NaN      NaN        NaN           108.0     NaN     NaN   128.0   \n",
+       "197        NaN     82.0        NaN             NaN     NaN     NaN   110.0   \n",
+       "198      103.0      NaN        NaN           137.0     NaN     NaN     NaN   \n",
+       "199      122.0      NaN        NaN            90.0     NaN     NaN     NaN   \n",
+       "\n",
+       "     darklings  alchemists  \n",
+       "0          NaN         NaN  \n",
+       "1        122.0         NaN  \n",
+       "2          NaN         NaN  \n",
+       "3         78.0         NaN  \n",
+       "4        128.0         NaN  \n",
+       "..         ...         ...  \n",
+       "195       87.0         NaN  \n",
+       "196       53.0         NaN  \n",
+       "197      117.0         NaN  \n",
+       "198       61.0         NaN  \n",
+       "199      120.0         NaN  \n",
+       "\n",
+       "[200 rows x 15 columns]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# sense check for 100 games\n",
+    "vpdf, _, _ = makenewdf()\n",
+    "featdf, _ = emptyfeaturesdf()\n",
+    "gamelengthlen = len(gameslist)\n",
+    "gamesroundup = math.ceil(gamelengthlen / 100.0) * 100\n",
+    "jj = 0\n",
+    "\n",
+    "for ii in range(100, 201, 100):     \n",
+    "    ii = min(ii, gamelengthlen)    \n",
+    "    print(f'Progressed to {ii}th game')\n",
+    "    \n",
+    "    next100games = gameslist[jj:ii]\n",
+    "    jj = ii  # so that we don't get any repetitions at the very end, where our set will be smaller\n",
+    "    \n",
+    "    gameevents100 = gameevents[gameevents['game'].isin(next100games)]\n",
+    "    gamemeta100 = games[games['game'].isin(next100games)]\n",
+    "    gameST100 = gamescoringtiles[gamescoringtiles['game'].isin(next100games)]\n",
+    "    \n",
+    "    for game in next100games:\n",
+    "        singlegame = gameevents100[gameevents100['game'] == game]\n",
+    "        singlegamemeta = gamemeta100[gamemeta100['game'] == game]\n",
+    "        singlegameST = gameST100[gameST100['game'] == game]\n",
+    "        \n",
+    "        if not len(singlegame) == 0:        \n",
+    "            vpforgame = get_vp_from_game(singlegame)\n",
+    "            featsforgame = get_features_from_game(singlegame, singlegamemeta, singlegameST)\n",
+    "            \n",
+    "            vpdf = vpdf.append(vpforgame, ignore_index=True)\n",
+    "            featdf = featdf.append(featsforgame, ignore_index=True)\n",
+    "    \n",
+    "print(f\"no of unique games in table is: {len(list(pd.unique(vpdf['game'])))}\")\n",
+    "vpdf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>game</th>\n",
+       "      <th>r1_SCORE1</th>\n",
+       "      <th>r1_SCORE2</th>\n",
+       "      <th>r1_SCORE3</th>\n",
+       "      <th>r1_SCORE4</th>\n",
+       "      <th>r1_SCORE5</th>\n",
+       "      <th>r1_SCORE6</th>\n",
+       "      <th>r1_SCORE7</th>\n",
+       "      <th>r1_SCORE8</th>\n",
+       "      <th>r1_SCORE9</th>\n",
+       "      <th>...</th>\n",
+       "      <th>BON8</th>\n",
+       "      <th>BON9</th>\n",
+       "      <th>BON10</th>\n",
+       "      <th>2players</th>\n",
+       "      <th>3players</th>\n",
+       "      <th>4players</th>\n",
+       "      <th>5players</th>\n",
+       "      <th>map1</th>\n",
+       "      <th>map2</th>\n",
+       "      <th>map3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0000000001</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>050615</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0512</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>051501</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>060303</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>195</th>\n",
+       "      <td>4pLeague_S7_D6L17_G7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>196</th>\n",
+       "      <td>4pLeague_S7_D6L18_G2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>197</th>\n",
+       "      <td>4pLeague_S7_D6L18_G5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>198</th>\n",
+       "      <td>4pLeague_S7_D6L18_G6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>199</th>\n",
+       "      <td>4pLeague_S7_D6L18_G7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>200 rows × 72 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     game r1_SCORE1 r1_SCORE2 r1_SCORE3 r1_SCORE4 r1_SCORE5  \\\n",
+       "0              0000000001         0         0         0         0         0   \n",
+       "1                  050615         0         0         0         0         1   \n",
+       "2                    0512         0         0         0         0         1   \n",
+       "3                  051501         0         0         0         0         0   \n",
+       "4                  060303         0         1         0         0         0   \n",
+       "..                    ...       ...       ...       ...       ...       ...   \n",
+       "195  4pLeague_S7_D6L17_G7         0         0         0         0         0   \n",
+       "196  4pLeague_S7_D6L18_G2         0         0         0         0         0   \n",
+       "197  4pLeague_S7_D6L18_G5         0         0         0         0         0   \n",
+       "198  4pLeague_S7_D6L18_G6         0         0         0         0         0   \n",
+       "199  4pLeague_S7_D6L18_G7         0         0         0         0         0   \n",
+       "\n",
+       "    r1_SCORE6 r1_SCORE7 r1_SCORE8 r1_SCORE9  ... BON8 BON9 BON10 2players  \\\n",
+       "0           0         0         1         0  ...    0    1     1        1   \n",
+       "1           0         0         0         0  ...    1    1     0        0   \n",
+       "2           0         0         0         0  ...    1    1     0        0   \n",
+       "3           0         0         1         0  ...    1    0     0        0   \n",
+       "4           0         0         0         0  ...    1    1     1        0   \n",
+       "..        ...       ...       ...       ...  ...  ...  ...   ...      ...   \n",
+       "195         0         1         0         0  ...    1    1     1        0   \n",
+       "196         0         1         0         0  ...    1    0     1        0   \n",
+       "197         0         0         1         0  ...    1    1     0        0   \n",
+       "198         0         0         1         0  ...    1    0     1        0   \n",
+       "199         0         0         1         0  ...    1    1     1        0   \n",
+       "\n",
+       "    3players 4players 5players map1 map2 map3  \n",
+       "0          0        0        0    1    0    0  \n",
+       "1          0        1        0    0    0    1  \n",
+       "2          0        1        0    1    0    0  \n",
+       "3          0        0        1    1    0    0  \n",
+       "4          0        1        0    0    1    0  \n",
+       "..       ...      ...      ...  ...  ...  ...  \n",
+       "195        0        1        0    1    0    0  \n",
+       "196        0        1        0    1    0    0  \n",
+       "197        0        1        0    1    0    0  \n",
+       "198        0        1        0    1    0    0  \n",
+       "199        0        1        0    1    0    0  \n",
+       "\n",
+       "[200 rows x 72 columns]"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "featdf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Looks good! We can soon do it with the full set of data... after we've done some cleaning.\n",
+    "\n",
+    "## Cleaning and filtering the data <a name=\"cleanUp\"></a>  \n",
+    "\n",
+    "Filtering bad data. We'll need to check... \n",
+    "1. gameevents have the same game list as games <span style=\"color:green\">- DONE</span>\n",
+    "2. the game has finished <span style=\"color:green\"> - DONE</span><span style=\"color:orange\"> -  more naunced: dropouts will change the feature, \"no. of players\". Therefore \"no. of players\" will be assigned to the end no. of players (after dropout) </span>\n",
+    "3. the map used (only use a sub-set of maps, or we could one-hot a list of accepted maps in X (features) <span style=\"color:green\">- DONE</span>\n",
+    "4. additional rules? Look up <span style=\"color:orange\">- left for now as v bitty: will add a bit of noise but not a lot</span>\n",
+    "5. Number of players. Possible for 2, 3, 4 or 5 player games, we could one-hot the differences as there might be similarities in different players that can contribute to predictions between different player games. Although >5 players should be removed. <span style=\"color:green\"> - DONE</span>\n",
+    "6. No extra factions (shapeshifters etc)   <span style=\"color:orange\"> - more naunced: these will be filtered out at the stage of making the data, using \"valid factions\". However we don't want to filter out any games where a valid faction is playing against an extra faction (we just won't include that faction's vp data). So we include it in the \"player dropped\" group.</span>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = dict()\n",
+    "data['gameevents'] = gameevents\n",
+    "data['games'] = games\n",
+    "data['gamescoringtiles'] = gamescoringtiles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def filteringByBadgames(data, badgames):\n",
+    "    \"\"\" Data is a dict containing gameevents, games, gamescoringtiles\n",
+    "        badgames is a pd.dataframe that contains ['game'] to filter by\n",
+    "    \"\"\"\n",
+    "    gameeventsfil = data['gameevents']\n",
+    "    gamesfil = data['games']\n",
+    "    gamescoringtilesfil = data['gamescoringtiles']\n",
+    "    \n",
+    "    badgameslist = badgames['game']\n",
+    "    gameeventsfilbefore = len(gameeventsfil)\n",
+    "    gamesbefore = len(gamesfil)\n",
+    "    gameSTbefore = len(gamescoringtilesfil)\n",
+    "\n",
+    "    gameeventsfil = gameeventsfil[~gameeventsfil['game'].isin(badgameslist)]\n",
+    "    gamesfil = gamesfil[~gamesfil['game'].isin(badgameslist)]\n",
+    "    gamescoringtilesfil = gamescoringtilesfil[~gamescoringtilesfil['game'].isin(badgameslist)]\n",
+    "\n",
+    "    print(f'game events before: {gameeventsfilbefore}, game events after: {len(gameeventsfil)}, game events removed: {gameeventsfilbefore-len(gameeventsfil)}')\n",
+    "    print(f'games before: {gamesbefore}, games after: {len(gamesfil)}, games removed: {gamesbefore-len(gamesfil)}')\n",
+    "    print(f'gameST before: {gameSTbefore}, gameST after: {len(gamescoringtilesfil)}, games removed: {gameSTbefore-len(gamescoringtilesfil)}')\n",
+    "    \n",
+    "    data['gameevents'] = gameeventsfil\n",
+    "    data['games'] = gamesfil\n",
+    "    data['gamescoringtiles'] = gamescoringtilesfil\n",
+    "    \n",
+    "    return data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### `Gameevents` games are same as `games` games"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# games from game_events are in games:\n",
+    "gamesymmetricdif = set(gameslist) ^ set(games['game'])\n",
+    "len(gamesymmetricdif)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So we have all the same games. Cool."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Removing the 1, 6, 7 player games\n",
+    "This was a problem noted here: https://www.kaggle.com/lemonkoala/some-faulty-data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>base_map</th>\n",
+       "      <th>game</th>\n",
+       "      <th>last_update</th>\n",
+       "      <th>player_count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1267</th>\n",
+       "      <td>be8f6ebf549404d015547152d5f2a1906ae8dd90</td>\n",
+       "      <td>HenriMustGoDown</td>\n",
+       "      <td>2015-07-10 06:03:39</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3110</th>\n",
+       "      <td>be8f6ebf549404d015547152d5f2a1906ae8dd90</td>\n",
+       "      <td>7pmatch3</td>\n",
+       "      <td>2015-11-23 17:34:26</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3896</th>\n",
+       "      <td>be8f6ebf549404d015547152d5f2a1906ae8dd90</td>\n",
+       "      <td>GTMDHXD</td>\n",
+       "      <td>2015-11-02 05:54:08</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4760</th>\n",
+       "      <td>95a66999127893f5925a5f591d54f8bcb9a670e6</td>\n",
+       "      <td>seven11</td>\n",
+       "      <td>2015-11-26 10:38:09</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5728</th>\n",
+       "      <td>be8f6ebf549404d015547152d5f2a1906ae8dd90</td>\n",
+       "      <td>6playerGregvlwonthelastone</td>\n",
+       "      <td>2016-01-11 14:43:19</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>112011</th>\n",
+       "      <td>126fe960806d587c78546b30f1a90853b1ada468</td>\n",
+       "      <td>7pmatch</td>\n",
+       "      <td>2015-08-22 14:35:38</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>112930</th>\n",
+       "      <td>be8f6ebf549404d015547152d5f2a1906ae8dd90</td>\n",
+       "      <td>MEGA2KING</td>\n",
+       "      <td>2015-08-26 12:21:04</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>113411</th>\n",
+       "      <td>126fe960806d587c78546b30f1a90853b1ada468</td>\n",
+       "      <td>ScruffyLookingNerfHerder</td>\n",
+       "      <td>2015-08-18 02:13:18</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>113813</th>\n",
+       "      <td>be8f6ebf549404d015547152d5f2a1906ae8dd90</td>\n",
+       "      <td>youmi023</td>\n",
+       "      <td>2015-08-27 08:07:33</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>113846</th>\n",
+       "      <td>126fe960806d587c78546b30f1a90853b1ada468</td>\n",
+       "      <td>YutoriGoGo009</td>\n",
+       "      <td>2015-08-29 00:32:15</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>83 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                        base_map                        game  \\\n",
+       "1267    be8f6ebf549404d015547152d5f2a1906ae8dd90             HenriMustGoDown   \n",
+       "3110    be8f6ebf549404d015547152d5f2a1906ae8dd90                    7pmatch3   \n",
+       "3896    be8f6ebf549404d015547152d5f2a1906ae8dd90                     GTMDHXD   \n",
+       "4760    95a66999127893f5925a5f591d54f8bcb9a670e6                     seven11   \n",
+       "5728    be8f6ebf549404d015547152d5f2a1906ae8dd90  6playerGregvlwonthelastone   \n",
+       "...                                          ...                         ...   \n",
+       "112011  126fe960806d587c78546b30f1a90853b1ada468                     7pmatch   \n",
+       "112930  be8f6ebf549404d015547152d5f2a1906ae8dd90                   MEGA2KING   \n",
+       "113411  126fe960806d587c78546b30f1a90853b1ada468    ScruffyLookingNerfHerder   \n",
+       "113813  be8f6ebf549404d015547152d5f2a1906ae8dd90                    youmi023   \n",
+       "113846  126fe960806d587c78546b30f1a90853b1ada468               YutoriGoGo009   \n",
+       "\n",
+       "                last_update  player_count  \n",
+       "1267    2015-07-10 06:03:39             6  \n",
+       "3110    2015-11-23 17:34:26             7  \n",
+       "3896    2015-11-02 05:54:08             7  \n",
+       "4760    2015-11-26 10:38:09             7  \n",
+       "5728    2016-01-11 14:43:19             6  \n",
+       "...                     ...           ...  \n",
+       "112011  2015-08-22 14:35:38             7  \n",
+       "112930  2015-08-26 12:21:04             6  \n",
+       "113411  2015-08-18 02:13:18             6  \n",
+       "113813  2015-08-27 08:07:33             7  \n",
+       "113846  2015-08-29 00:32:15             7  \n",
+       "\n",
+       "[83 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "badgames = games[games[\"player_count\"].isin([1, 6, 7])]\n",
+    "badgames"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "game events before: 73419074, game events after: 73332500, game events removed: 86574\n",
+      "games before: 115612, games after: 115529, games removed: 83\n",
+      "gameST before: 693660, gameST after: 693162, games removed: 498\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = filteringByBadgames(data, badgames)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Checking the game has finished\n",
+    "To check this, we can look at game events. If every player has passed their bonus tile on round 6, they've all made it to the end. If not, they've dropped out."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['action:ACT1', 'action:ACT2', 'action:ACT4', 'action:FAV6',\n",
+       "       'advance:ship', 'bridge', 'build:D', 'burn', 'decline:count',\n",
+       "       'decline:pw', 'dig', 'favor:FAV12', 'favor:any', 'leech:count',\n",
+       "       'leech:pw', 'order:1', 'order:2', 'order:3', 'order:4', 'town:TW1',\n",
+       "       'town:TW2', 'town:TW5', 'town:any', 'upgrade:TE', 'upgrade:TP',\n",
+       "       'vp'], dtype=object)"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "singlegame = gameevents[gameevents['game'] == 'NewbiesWelcome12']\n",
+    "r6 = singlegame[singlegame['round'] == 6]\n",
+    "uniqueevents = np.sort(pd.unique(r6['event']))\n",
+    "uniqueevents"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There's no pass logged in round 6?! What?! What about round 5?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['action:ACT1', 'action:ACT3', 'action:ACT6', 'action:ACTS',\n",
+       "       'action:BON1', 'action:FAV6', 'bridge', 'build:D', 'burn', 'dig',\n",
+       "       'favor:FAV1', 'favor:FAV10', 'favor:FAV5', 'favor:FAV6',\n",
+       "       'favor:any', 'leech:count', 'leech:pw', 'order:1', 'order:2',\n",
+       "       'order:3', 'order:4', 'pass:BON2', 'pass:BON4', 'pass:BON7',\n",
+       "       'town:TW3', 'town:any', 'upgrade:SA', 'upgrade:TE', 'upgrade:TP',\n",
+       "       'vp'], dtype=object)"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "singlegame = gameevents[gameevents['game'] == 'NewbiesWelcome12']\n",
+    "r5 = singlegame[singlegame['round'] == 5]\n",
+    "uniqueevents = np.sort(pd.unique(r5['event']))\n",
+    "uniqueevents"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4388095    False\n",
+       "4388096    False\n",
+       "4388107    False\n",
+       "4388109    False\n",
+       "4388119    False\n",
+       "           ...  \n",
+       "4388605    False\n",
+       "4388607    False\n",
+       "4388612    False\n",
+       "4388618    False\n",
+       "4388619    False\n",
+       "Name: event, Length: 92, dtype: bool"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r6['event'].str.startswith('pass')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Okay. So there is in round 5 but not in round 6. Not a fool-proof way of checking no-one has dropped out but you'd hope if they made it to round 6, they wouldn't drop out then. Very annoying, we'll just base it off round 5 passes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "faction: swarmlings ended their turn?: True\n",
+      "faction: halflings ended their turn?: True\n",
+      "faction: engineers ended their turn?: False\n",
+      "faction: witches ended their turn?: True\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>game</th>\n",
+       "      <th>nodrops</th>\n",
+       "      <th>startplayers</th>\n",
+       "      <th>playersdropped</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>NewbiesWelcome12</td>\n",
+       "      <td>False</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               game  nodrops  startplayers  playersdropped\n",
+       "0  NewbiesWelcome12    False             4               1"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# sense-check - every player passed final round except engineers in this game\n",
+    "singlegame = gameevents[gameevents['game'] == 'NewbiesWelcome12']\n",
+    "\n",
+    "extendedfactions = validfactions + ['dragonlords', 'riverwalkers', 'yetis', 'icemaidens', 'shapeshifters', 'acolytes']\n",
+    "\n",
+    "def check_game_ended(singlegame, verbose=False):\n",
+    "    r5 = singlegame[singlegame['round'] == 5]\n",
+    "    rawfactions = pd.unique(singlegame['faction'])\n",
+    "    verifiedfactions = [rawfaction for rawfaction in rawfactions if rawfaction in extendedfactions]\n",
+    "    allbool = []\n",
+    "\n",
+    "    for faction in verifiedfactions:\n",
+    "        factionbool = len(r5[(r5['faction'] == faction)  & (r5['event'].str.startswith('pass'))]) == 1\n",
+    "        allbool.append(factionbool)\n",
+    "        \n",
+    "        if verbose:\n",
+    "            print(f'faction: {faction} ended their turn?: {factionbool}')\n",
+    "    \n",
+    "    isgood = all(allbool)\n",
+    "    startplayers = len(verifiedfactions)\n",
+    "    boolsum = sum(allbool)\n",
+    "    playersdropped = startplayers - boolsum\n",
+    "    \n",
+    "    return isgood, startplayers, playersdropped\n",
+    "\n",
+    "isgood, startplayers, playersdropped = check_game_ended(singlegame, verbose=True)\n",
+    "newdf = pd.DataFrame([['NewbiesWelcome12', isgood, startplayers, playersdropped]], columns=['game', 'nodrops', 'startplayers', 'playersdropped'])\n",
+    "newdf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll also need to check there even *is* a round 5 (or a round 6). If there isn't... clearly the game hasn't been finished. We might as well check nothing odd is going on and there isn't a round 7/8/9 either."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Progressed to 10000th game\n",
+      "Progressed to 20000th game\n",
+      "Progressed to 30000th game\n",
+      "Progressed to 40000th game\n",
+      "Progressed to 50000th game\n",
+      "Progressed to 60000th game\n",
+      "Progressed to 70000th game\n",
+      "Progressed to 80000th game\n",
+      "Progressed to 90000th game\n",
+      "Progressed to 100000th game\n",
+      "Progressed to 110000th game\n",
+      "no of unique games in table is: 115528\n"
+     ]
+    }
+   ],
+   "source": [
+    "gameevents = data['gameevents']\n",
+    "gameslist = list(pd.unique(gameevents['game']))\n",
+    "\n",
+    "gamelengthlen = len(gameslist)\n",
+    "gamesroundup = math.ceil(gamelengthlen / 100.0) * 100\n",
+    "jj = 0\n",
+    "playerdropdf = pd.DataFrame(columns=['game', 'nodrops', 'startplayers', 'playersdropped'])\n",
+    "\n",
+    "for ii in range(100, gamesroundup+1, 100):     \n",
+    "    ii = min(ii, gamelengthlen)    \n",
+    "    if (ii % 10000) == 0:  # update every 10000 games\n",
+    "        print(f'Progressed to {ii}th game')\n",
+    "    \n",
+    "    next100games = gameslist[jj:ii]\n",
+    "    jj = ii  # so that we don't get any repetitions at the very end, where our set will be smaller\n",
+    "    \n",
+    "    gameevents100 = gameevents[gameevents['game'].isin(next100games)]\n",
+    "\n",
+    "    for game in next100games:\n",
+    "        singlegame = gameevents100[gameevents100['game'] == game]\n",
+    "        \n",
+    "        if not len(singlegame) == 0:\n",
+    "            isgood, startplayers, playersdropped = check_game_ended(singlegame)\n",
+    "            newdf = pd.DataFrame([[game, isgood, startplayers, playersdropped]], columns=['game', 'nodrops', 'startplayers', 'playersdropped'])\n",
+    "            playerdropdf = playerdropdf.append(newdf, ignore_index=True)\n",
+    "\n",
+    "print(f\"no of unique games in table is: {len(list(pd.unique(playerdropdf['game'])))}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    100147\n",
+       "1     11975\n",
+       "2      2720\n",
+       "3       530\n",
+       "4       133\n",
+       "5        23\n",
+       "Name: playersdropped, dtype: int64"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "playerdropdf['playersdropped'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "playerdropdf['endplayers'] = playerdropdf['startplayers'] - playerdropdf['playersdropped']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4    57599\n",
+       "3    25708\n",
+       "2    23374\n",
+       "5     6057\n",
+       "1     1921\n",
+       "0      869\n",
+       "Name: endplayers, dtype: int64"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "playerdropdf['endplayers'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "playerdropdf.to_csv('D://PycharmProjects/TerraBot/data/faction-picker-bot/unfinishedgames.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# for coming back to edit\n",
+    "playerdropdf = pd.read_csv('D://PycharmProjects/TerraBot/data/faction-picker-bot/unfinishedgames.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Remove all the games which end up with 0 or 1 player."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>game</th>\n",
+       "      <th>nodrops</th>\n",
+       "      <th>startplayers</th>\n",
+       "      <th>playersdropped</th>\n",
+       "      <th>endplayers</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0000000001</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>050615</td>\n",
+       "      <td>True</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0512</td>\n",
+       "      <td>True</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>051501</td>\n",
+       "      <td>True</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>060303</td>\n",
+       "      <td>True</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115523</th>\n",
+       "      <td>115523</td>\n",
+       "      <td>Youreacrookcaptianhook</td>\n",
+       "      <td>False</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115524</th>\n",
+       "      <td>115524</td>\n",
+       "      <td>YourMomIsAChaosMagician</td>\n",
+       "      <td>True</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115525</th>\n",
+       "      <td>115525</td>\n",
+       "      <td>ytuwertqwtr</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115526</th>\n",
+       "      <td>115526</td>\n",
+       "      <td>yuertyqert</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115527</th>\n",
+       "      <td>115527</td>\n",
+       "      <td>ZeicheMasZeuchnis</td>\n",
+       "      <td>True</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>115528 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Unnamed: 0                     game  nodrops  startplayers  \\\n",
+       "0                0               0000000001     True             2   \n",
+       "1                1                   050615     True             4   \n",
+       "2                2                     0512     True             4   \n",
+       "3                3                   051501     True             5   \n",
+       "4                4                   060303     True             4   \n",
+       "...            ...                      ...      ...           ...   \n",
+       "115523      115523   Youreacrookcaptianhook    False             3   \n",
+       "115524      115524  YourMomIsAChaosMagician     True             3   \n",
+       "115525      115525              ytuwertqwtr     True             2   \n",
+       "115526      115526               yuertyqert     True             2   \n",
+       "115527      115527        ZeicheMasZeuchnis     True             3   \n",
+       "\n",
+       "        playersdropped  endplayers  \n",
+       "0                    0           2  \n",
+       "1                    0           4  \n",
+       "2                    0           4  \n",
+       "3                    0           5  \n",
+       "4                    0           4  \n",
+       "...                ...         ...  \n",
+       "115523               1           2  \n",
+       "115524               0           3  \n",
+       "115525               0           2  \n",
+       "115526               0           2  \n",
+       "115527               0           3  \n",
+       "\n",
+       "[115528 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "playerdropdf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "game events before: 73332500, game events after: 72827570, game events removed: 504930\n",
+      "games before: 115529, games after: 112739, games removed: 2790\n",
+      "gameST before: 693162, gameST after: 676434, games removed: 16728\n"
+     ]
+    }
+   ],
+   "source": [
+    "badgames = playerdropdf[playerdropdf['endplayers'].isin([0, 1])]\n",
+    "data = filteringByBadgames(data, badgames)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2790\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(len(playerdropdf[playerdropdf['endplayers'].isin([0, 1])]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Removing unwanted maps\n",
+    "We want these maps:  \n",
+    "126fe960806d587c78546b30f1a90853b1ada468 - map1  \n",
+    "95a66999127893f5925a5f591d54f8bcb9a670e6 - map2  \n",
+    "be8f6ebf549404d015547152d5f2a1906ae8dd90 - map3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "game events before: 72827570, game events after: 72067832, game events removed: 759738\n",
+      "games before: 112739, games after: 111547, games removed: 1192\n",
+      "gameST before: 676434, gameST after: 669282, games removed: 7152\n"
+     ]
+    }
+   ],
+   "source": [
+    "acceptablemaps = ['126fe960806d587c78546b30f1a90853b1ada468',   \n",
+    "                  '95a66999127893f5925a5f591d54f8bcb9a670e6', \n",
+    "                  'be8f6ebf549404d015547152d5f2a1906ae8dd90']\n",
+    "\n",
+    "badgames = games[~games[\"base_map\"].isin(acceptablemaps)]\n",
+    "data = filteringByBadgames(data, badgames)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating the full dataset <a name=\"fullDataset\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Progressed to 10000th game\n",
+      "Progressed to 20000th game\n",
+      "Progressed to 30000th game\n",
+      "Progressed to 40000th game\n",
+      "Progressed to 50000th game\n",
+      "Progressed to 60000th game\n",
+      "Progressed to 70000th game\n",
+      "Progressed to 80000th game\n",
+      "Progressed to 90000th game\n",
+      "Progressed to 100000th game\n",
+      "Progressed to 110000th game\n",
+      "no of unique games in table is: 111546\n"
+     ]
+    }
+   ],
+   "source": [
+    "vpdf, _, _ = makenewdf()\n",
+    "featdf, _ = emptyfeaturesdf()\n",
+    "\n",
+    "gameevents = data['gameevents']\n",
+    "games = data['games']\n",
+    "gamescoringtiles = data['gamescoringtiles']\n",
+    "\n",
+    "gameslist = list(pd.unique(gameevents['game']))\n",
+    "gamelengthlen = len(gameslist)\n",
+    "gamesroundup = math.ceil(gamelengthlen / 100.0) * 100\n",
+    "jj = 0\n",
+    "\n",
+    "for ii in range(100, gamesroundup+1, 100):     \n",
+    "    ii = min(ii, gamelengthlen)    \n",
+    "    if (ii % 10000) == 0:  # update every 10000 games\n",
+    "        print(f'Progressed to {ii}th game')\n",
+    "    \n",
+    "    next100games = gameslist[jj:ii]\n",
+    "    jj = ii  # so that we don't get any repetitions at the very end, where our set will be smaller\n",
+    "    \n",
+    "    gameevents100 = gameevents[gameevents['game'].isin(next100games)]\n",
+    "    gamemeta100 = games[games['game'].isin(next100games)]\n",
+    "    gameST100 = gamescoringtiles[gamescoringtiles['game'].isin(next100games)]\n",
+    "    endplayers100 = playerdropdf[playerdropdf['game'].isin(next100games)]  # use this for player count\n",
+    "\n",
+    "    for game in next100games:\n",
+    "        singlegame = gameevents100[gameevents100['game'] == game]\n",
+    "        singlegamemeta = gamemeta100[gamemeta100['game'] == game]\n",
+    "        singlegameST = gameST100[gameST100['game'] == game]\n",
+    "        singleendplayers = endplayers100[endplayers100['game'] == game]\n",
+    "        \n",
+    "        if not len(singlegame) == 0:\n",
+    "            vpforgame = get_vp_from_game(singlegame)\n",
+    "            featsforgame = get_features_from_game(singlegame, singlegamemeta, singlegameST, singleendplayers=singleendplayers)\n",
+    "            \n",
+    "            vpdf = vpdf.append(vpforgame, ignore_index=True)\n",
+    "            featdf = featdf.append(featsforgame, ignore_index=True)\n",
+    "\n",
+    "print(f\"no of unique games in table is: {len(list(pd.unique(vpdf['game'])))}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>game</th>\n",
+       "      <th>witches</th>\n",
+       "      <th>auren</th>\n",
+       "      <th>swarmlings</th>\n",
+       "      <th>mermaids</th>\n",
+       "      <th>cultists</th>\n",
+       "      <th>halflings</th>\n",
+       "      <th>dwarves</th>\n",
+       "      <th>engineers</th>\n",
+       "      <th>chaosmagicians</th>\n",
+       "      <th>giants</th>\n",
+       "      <th>fakirs</th>\n",
+       "      <th>nomads</th>\n",
+       "      <th>darklings</th>\n",
+       "      <th>alchemists</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>118.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>62.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>050615</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0512</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>124.0</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>051501</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>78.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>060303</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>108.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>121.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>78.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
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+       "    <tr>\n",
+       "      <th>111541</th>\n",
+       "      <td>Youreacrookcaptianhook</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>126.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111542</th>\n",
+       "      <td>YourMomIsAChaosMagician</td>\n",
+       "      <td>112.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>114.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111543</th>\n",
+       "      <td>ytuwertqwtr</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>102.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111544</th>\n",
+       "      <td>yuertyqert</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>141.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111545</th>\n",
+       "      <td>ZeicheMasZeuchnis</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>132.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>88.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>111546 rows × 15 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                           game  witches  auren  swarmlings  mermaids  \\\n",
+       "0                    0000000001      NaN    NaN         NaN     118.0   \n",
+       "1                        050615      NaN    NaN         NaN       NaN   \n",
+       "2                          0512      NaN    NaN         NaN       NaN   \n",
+       "3                        051501      NaN   84.0         NaN       NaN   \n",
+       "4                        060303      NaN    NaN         NaN     108.0   \n",
+       "...                         ...      ...    ...         ...       ...   \n",
+       "111541   Youreacrookcaptianhook      NaN    NaN       126.0       NaN   \n",
+       "111542  YourMomIsAChaosMagician    112.0    NaN       114.0       NaN   \n",
+       "111543              ytuwertqwtr      NaN    NaN       105.0       NaN   \n",
+       "111544               yuertyqert    128.0    NaN         NaN       NaN   \n",
+       "111545        ZeicheMasZeuchnis      NaN    NaN         NaN       NaN   \n",
+       "\n",
+       "        cultists  halflings  dwarves  engineers  chaosmagicians  giants  \\\n",
+       "0            NaN       62.0      NaN        NaN             NaN     NaN   \n",
+       "1            NaN        NaN      NaN        NaN             NaN     NaN   \n",
+       "2            NaN        NaN      NaN      124.0           101.0     NaN   \n",
+       "3            NaN       77.0      NaN        NaN            72.0     NaN   \n",
+       "4            NaN        NaN      NaN      121.0             NaN     NaN   \n",
+       "...          ...        ...      ...        ...             ...     ...   \n",
+       "111541       NaN      105.0      NaN        NaN             NaN     NaN   \n",
+       "111542       NaN        NaN      NaN        NaN             NaN     NaN   \n",
+       "111543       NaN        NaN    102.0        NaN             NaN     NaN   \n",
+       "111544       NaN        NaN      NaN        NaN           141.0     NaN   \n",
+       "111545       NaN        NaN    132.0        NaN             NaN     NaN   \n",
+       "\n",
+       "        fakirs  nomads  darklings  alchemists  \n",
+       "0          NaN     NaN        NaN         NaN  \n",
+       "1          NaN     NaN      122.0         NaN  \n",
+       "2          NaN     NaN        NaN         NaN  \n",
+       "3          NaN    54.0       78.0         NaN  \n",
+       "4         78.0     NaN      128.0         NaN  \n",
+       "...        ...     ...        ...         ...  \n",
+       "111541     NaN    37.0        NaN         NaN  \n",
+       "111542     NaN   120.0        NaN         NaN  \n",
+       "111543     NaN     NaN        NaN         NaN  \n",
+       "111544     NaN     NaN        NaN         NaN  \n",
+       "111545     NaN    88.0      120.0         NaN  \n",
+       "\n",
+       "[111546 rows x 15 columns]"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vpdf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111544</th>\n",
+       "      <td>yuertyqert</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111545</th>\n",
+       "      <td>ZeicheMasZeuchnis</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>111546 rows × 72 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                           game r1_SCORE1 r1_SCORE2 r1_SCORE3 r1_SCORE4  \\\n",
+       "0                    0000000001         0         0         0         0   \n",
+       "1                        050615         0         0         0         0   \n",
+       "2                          0512         0         0         0         0   \n",
+       "3                        051501         0         0         0         0   \n",
+       "4                        060303         0         1         0         0   \n",
+       "...                         ...       ...       ...       ...       ...   \n",
+       "111541   Youreacrookcaptianhook         0         0         0         0   \n",
+       "111542  YourMomIsAChaosMagician         0         0         1         0   \n",
+       "111543              ytuwertqwtr         1         0         0         0   \n",
+       "111544               yuertyqert         0         0         0         1   \n",
+       "111545        ZeicheMasZeuchnis         0         0         1         0   \n",
+       "\n",
+       "       r1_SCORE5 r1_SCORE6 r1_SCORE7 r1_SCORE8 r1_SCORE9  ... BON8 BON9 BON10  \\\n",
+       "0              0         0         0         1         0  ...    0    1     1   \n",
+       "1              1         0         0         0         0  ...    1    1     0   \n",
+       "2              1         0         0         0         0  ...    1    1     0   \n",
+       "3              0         0         0         1         0  ...    1    0     0   \n",
+       "4              0         0         0         0         0  ...    1    1     1   \n",
+       "...          ...       ...       ...       ...       ...  ...  ...  ...   ...   \n",
+       "111541         0         1         0         0         0  ...    0    1     0   \n",
+       "111542         0         0         0         0         0  ...    1    1     0   \n",
+       "111543         0         0         0         0         0  ...    1    1     1   \n",
+       "111544         0         0         0         0         0  ...    1    0     1   \n",
+       "111545         0         0         0         0         0  ...    1    1     0   \n",
+       "\n",
+       "       2players 3players 4players 5players map1 map2 map3  \n",
+       "0             1        0        0        0    1    0    0  \n",
+       "1             0        0        1        0    0    0    1  \n",
+       "2             0        0        1        0    1    0    0  \n",
+       "3             0        0        0        1    1    0    0  \n",
+       "4             0        0        1        0    0    1    0  \n",
+       "...         ...      ...      ...      ...  ...  ...  ...  \n",
+       "111541        1        0        0        0    1    0    0  \n",
+       "111542        0        1        0        0    1    0    0  \n",
+       "111543        1        0        0        0    1    0    0  \n",
+       "111544        1        0        0        0    1    0    0  \n",
+       "111545        0        1        0        0    1    0    0  \n",
+       "\n",
+       "[111546 rows x 72 columns]"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "featdf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vpdf.to_csv('D://PycharmProjects/TerraBot/data/faction-picker-bot/vpdata.csv')\n",
+    "featdf.to_csv('D://PycharmProjects/TerraBot/data/faction-picker-bot/featdata.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# check we still have the same games:\n",
+    "gamesymmetricdif = set(vpdf['game']) ^ set(featdf['game'])\n",
+    "len(gamesymmetricdif)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/faction-picker-bot/.ipynb_checkpoints/individual_faction_data-checkpoint.ipynb b/faction-picker-bot/.ipynb_checkpoints/individual_faction_data-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..98c996df81e27f01623360e1fa4b06aebe65d6a2
--- /dev/null
+++ b/faction-picker-bot/.ipynb_checkpoints/individual_faction_data-checkpoint.ipynb
@@ -0,0 +1,1087 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "2f27067c-4b22-4f2e-b467-427467475fa6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import math\n",
+    "import time\n",
+    "import yaml\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "df1442eb-fb81-43c5-9b22-2cff7b794da0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'D:\\\\PycharmProjects\\\\TerraBot\\\\faction-picker-bot'"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "_dh[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "708c5ce7-eed3-4449-9140-1f41e8865b66",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['In',\n",
+       " 'Out',\n",
+       " '_',\n",
+       " '_11',\n",
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+       " '__builtins__',\n",
+       " '__doc__',\n",
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+       " '__package__',\n",
+       " '__spec__',\n",
+       " '_dh',\n",
+       " '_i',\n",
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+       " '_i11',\n",
+       " '_i12',\n",
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+       " '_ih',\n",
+       " '_ii',\n",
+       " '_iii',\n",
+       " '_oh',\n",
+       " 'allmermaids',\n",
+       " 'colnames',\n",
+       " 'each_faction_dataset',\n",
+       " 'exit',\n",
+       " 'faction',\n",
+       " 'faction_dataset',\n",
+       " 'factions',\n",
+       " 'featdf',\n",
+       " 'get_ipython',\n",
+       " 'indexes',\n",
+       " 'indexs',\n",
+       " 'math',\n",
+       " 'np',\n",
+       " 'os',\n",
+       " 'pd',\n",
+       " 'quit',\n",
+       " 'time',\n",
+       " 'vpdf',\n",
+       " 'yaml']"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "b51ade6d-f3df-4c59-95bb-f1e1df99cf75",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "script_dir = os.path.dirname(_dh[0])\n",
+    "filename = os.path.join(script_dir, 'params.yaml')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "7fdcd13e-011c-4349-86b1-f00098edc00c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'D:\\\\PycharmProjects\\\\TerraBot\\\\params.yaml'"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filename"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "abc42db1-fdda-48f9-ac22-73ab04e07658",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'yaml' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-2-6d8b02a8dfed>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"../params.yaml\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'r'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mfd\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[0mparams\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0myaml\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msafe_load\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfd\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m: name 'yaml' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "with open(\"../params.yaml\", 'r') as fd:\n",
+    "    params = yaml.safe_load(fd)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "941150cc-5223-4984-8981-808021c5ce55",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "<style scoped>\n",
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+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>game</th>\n",
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+       "      <th>mermaids</th>\n",
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+       "    </tr>\n",
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+       "      <th>0</th>\n",
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+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111541</th>\n",
+       "      <td>111541</td>\n",
+       "      <td>Youreacrookcaptianhook</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>126.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111542</th>\n",
+       "      <td>111542</td>\n",
+       "      <td>YourMomIsAChaosMagician</td>\n",
+       "      <td>112.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>114.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111543</th>\n",
+       "      <td>111543</td>\n",
+       "      <td>ytuwertqwtr</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>102.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111544</th>\n",
+       "      <td>111544</td>\n",
+       "      <td>yuertyqert</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>141.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111545</th>\n",
+       "      <td>111545</td>\n",
+       "      <td>ZeicheMasZeuchnis</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>132.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>88.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>111546 rows × 16 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Unnamed: 0                     game  witches  auren  swarmlings  \\\n",
+       "0                0               0000000001      NaN    NaN         NaN   \n",
+       "1                1                   050615      NaN    NaN         NaN   \n",
+       "2                2                     0512      NaN    NaN         NaN   \n",
+       "3                3                   051501      NaN   84.0         NaN   \n",
+       "4                4                   060303      NaN    NaN         NaN   \n",
+       "...            ...                      ...      ...    ...         ...   \n",
+       "111541      111541   Youreacrookcaptianhook      NaN    NaN       126.0   \n",
+       "111542      111542  YourMomIsAChaosMagician    112.0    NaN       114.0   \n",
+       "111543      111543              ytuwertqwtr      NaN    NaN       105.0   \n",
+       "111544      111544               yuertyqert    128.0    NaN         NaN   \n",
+       "111545      111545        ZeicheMasZeuchnis      NaN    NaN         NaN   \n",
+       "\n",
+       "        mermaids  cultists  halflings  dwarves  engineers  chaosmagicians  \\\n",
+       "0          118.0       NaN       62.0      NaN        NaN             NaN   \n",
+       "1            NaN       NaN        NaN      NaN        NaN             NaN   \n",
+       "2            NaN       NaN        NaN      NaN      124.0           101.0   \n",
+       "3            NaN       NaN       77.0      NaN        NaN            72.0   \n",
+       "4          108.0       NaN        NaN      NaN      121.0             NaN   \n",
+       "...          ...       ...        ...      ...        ...             ...   \n",
+       "111541       NaN       NaN      105.0      NaN        NaN             NaN   \n",
+       "111542       NaN       NaN        NaN      NaN        NaN             NaN   \n",
+       "111543       NaN       NaN        NaN    102.0        NaN             NaN   \n",
+       "111544       NaN       NaN        NaN      NaN        NaN           141.0   \n",
+       "111545       NaN       NaN        NaN    132.0        NaN             NaN   \n",
+       "\n",
+       "        giants  fakirs  nomads  darklings  alchemists  \n",
+       "0          NaN     NaN     NaN        NaN         NaN  \n",
+       "1          NaN     NaN     NaN      122.0         NaN  \n",
+       "2          NaN     NaN     NaN        NaN         NaN  \n",
+       "3          NaN     NaN    54.0       78.0         NaN  \n",
+       "4          NaN    78.0     NaN      128.0         NaN  \n",
+       "...        ...     ...     ...        ...         ...  \n",
+       "111541     NaN     NaN    37.0        NaN         NaN  \n",
+       "111542     NaN     NaN   120.0        NaN         NaN  \n",
+       "111543     NaN     NaN     NaN        NaN         NaN  \n",
+       "111544     NaN     NaN     NaN        NaN         NaN  \n",
+       "111545     NaN     NaN    88.0      120.0         NaN  \n",
+       "\n",
+       "[111546 rows x 16 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vpdf = pd.read_csv('D://PycharmProjects/TerraBot/data/faction-picker-bot/vpdata.csv')\n",
+    "featdf = pd.read_csv('D://PycharmProjects/TerraBot/data/faction-picker-bot/featdata.csv')\n",
+    "\n",
+    "# split in 12 faction data\n",
+    "vpdf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "db0b961f-c751-439d-8583-da459db2d6d6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "colnames = list(vpdf.columns)\n",
+    "factions = [x for x in colnames if x != 'game' and x != 'Unnamed: 0']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "657af1ef-3338-4c27-b7a2-dbfe7ab959ed",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0         False\n",
+       "1          True\n",
+       "2          True\n",
+       "3          True\n",
+       "4         False\n",
+       "          ...  \n",
+       "111541     True\n",
+       "111542     True\n",
+       "111543     True\n",
+       "111544     True\n",
+       "111545     True\n",
+       "Name: mermaids, Length: 111546, dtype: bool"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "indexs = pd.isnull(vpdf['mermaids'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "5ed90fe5-75b1-47db-957d-deeeaf750f02",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "allmermaids = vpdf['mermaids'][~indexs]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "788e7f43-8e25-4b53-b4be-98eefee1ae85",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0         118.0\n",
+       "4         108.0\n",
+       "6         104.0\n",
+       "9         106.0\n",
+       "21        140.0\n",
+       "          ...  \n",
+       "111522     98.0\n",
+       "111525     90.0\n",
+       "111535     68.0\n",
+       "111537     85.0\n",
+       "111538    136.0\n",
+       "Name: mermaids, Length: 33442, dtype: float64"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "allmermaids"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "282694f1-2c1a-4b55-920a-791d50ea7946",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>game</th>\n",
+       "      <th>r1_SCORE1</th>\n",
+       "      <th>r1_SCORE2</th>\n",
+       "      <th>r1_SCORE3</th>\n",
+       "      <th>r1_SCORE4</th>\n",
+       "      <th>r1_SCORE5</th>\n",
+       "      <th>r1_SCORE6</th>\n",
+       "      <th>r1_SCORE7</th>\n",
+       "      <th>r1_SCORE8</th>\n",
+       "      <th>...</th>\n",
+       "      <th>BON8</th>\n",
+       "      <th>BON9</th>\n",
+       "      <th>BON10</th>\n",
+       "      <th>2players</th>\n",
+       "      <th>3players</th>\n",
+       "      <th>4players</th>\n",
+       "      <th>5players</th>\n",
+       "      <th>map1</th>\n",
+       "      <th>map2</th>\n",
+       "      <th>map3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0000000001</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>060303</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6</td>\n",
+       "      <td>070315</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>9</td>\n",
+       "      <td>0pandoraravenclaw</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>21</td>\n",
+       "      <td>16JULY2015</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111522</th>\n",
+       "      <td>111522</td>\n",
+       "      <td>WelcomeFastPlayer</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111525</th>\n",
+       "      <td>111525</td>\n",
+       "      <td>WhatsForLunch</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111535</th>\n",
+       "      <td>111535</td>\n",
+       "      <td>XiangIsDumb</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111537</th>\n",
+       "      <td>111537</td>\n",
+       "      <td>XxxX01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111538</th>\n",
+       "      <td>111538</td>\n",
+       "      <td>Yellowsubmarine</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>33442 rows × 73 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Unnamed: 0               game  r1_SCORE1  r1_SCORE2  r1_SCORE3  \\\n",
+       "0                0         0000000001          0          0          0   \n",
+       "4                4             060303          0          1          0   \n",
+       "6                6             070315          0          1          0   \n",
+       "9                9  0pandoraravenclaw          0          1          0   \n",
+       "21              21         16JULY2015          0          0          0   \n",
+       "...            ...                ...        ...        ...        ...   \n",
+       "111522      111522  WelcomeFastPlayer          0          0          0   \n",
+       "111525      111525      WhatsForLunch          0          0          0   \n",
+       "111535      111535        XiangIsDumb          0          0          1   \n",
+       "111537      111537             XxxX01          1          0          0   \n",
+       "111538      111538    Yellowsubmarine          0          0          1   \n",
+       "\n",
+       "        r1_SCORE4  r1_SCORE5  r1_SCORE6  r1_SCORE7  r1_SCORE8  ...  BON8  \\\n",
+       "0               0          0          0          0          1  ...     0   \n",
+       "4               0          0          0          0          0  ...     1   \n",
+       "6               0          0          0          0          0  ...     1   \n",
+       "9               0          0          0          0          0  ...     0   \n",
+       "21              0          1          0          0          0  ...     0   \n",
+       "...           ...        ...        ...        ...        ...  ...   ...   \n",
+       "111522          0          1          0          0          0  ...     1   \n",
+       "111525          0          1          0          0          0  ...     1   \n",
+       "111535          0          0          0          0          0  ...     1   \n",
+       "111537          0          0          0          0          0  ...     1   \n",
+       "111538          0          0          0          0          0  ...     0   \n",
+       "\n",
+       "        BON9  BON10  2players  3players  4players  5players  map1  map2  map3  \n",
+       "0          1      1         1         0         0         0     1     0     0  \n",
+       "4          1      1         0         0         1         0     0     1     0  \n",
+       "6          1      1         0         0         1         0     1     0     0  \n",
+       "9          0      0         1         0         0         0     1     0     0  \n",
+       "21         1      0         0         0         1         0     1     0     0  \n",
+       "...      ...    ...       ...       ...       ...       ...   ...   ...   ...  \n",
+       "111522     1      0         0         0         1         0     1     0     0  \n",
+       "111525     0      0         1         0         0         0     1     0     0  \n",
+       "111535     1      0         0         1         0         0     1     0     0  \n",
+       "111537     0      1         0         1         0         0     1     0     0  \n",
+       "111538     1      1         0         1         0         0     1     0     0  \n",
+       "\n",
+       "[33442 rows x 73 columns]"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "featdf[~indexs]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "a101ac03-1fe7-452f-a7b2-ad702136de81",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "each_faction_dataset = {}\n",
+    "for faction in factions:\n",
+    "    faction_dataset = {}\n",
+    "    indexes = pd.isnull(vpdf[faction])\n",
+    "    faction_dataset['vp'] = vpdf[faction][~indexes]\n",
+    "    faction_dataset['features'] = featdf[~indexs]\n",
+    "    each_faction_dataset[faction] = faction_dataset\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "60fd33be-c671-4112-9112-22b38df7fe08",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/faction-picker-bot/.ipynb_checkpoints/make_data-checkpoint.py b/faction-picker-bot/.ipynb_checkpoints/make_data-checkpoint.py
new file mode 100644
index 0000000000000000000000000000000000000000..58edae177bd20b902b03ac68530580496e5001a5
--- /dev/null
+++ b/faction-picker-bot/.ipynb_checkpoints/make_data-checkpoint.py
@@ -0,0 +1,178 @@
+import pandas as pd
+import numpy as np
+import math
+import time
+
+def main():
+    """This is the script version of creatingVPdata.ipynb for the dvc pipeline."""
+    # load data in
+    folderlocation = "D:/PycharmProjects/TerraBot/terra-mystica"
+    gameevents = pd.read_csv(f'{folderlocation}/game_events.csv')
+    games = pd.read_csv(f'{folderlocation}/games.csv')
+    gameslist = list(pd.unique(gameevents['game']))
+    allfactions = pd.unique(gameevents['faction'])
+    gamescoringtiles = pd.read_csv(f'{folderlocation}/game_scoring_tiles.csv')
+    gameoptions = pd.read_csv(f'{folderlocation}/game_options.csv')
+    stats = pd.read_csv(f'{folderlocation}/stats.csv')
+
+    # two vp dataset functions
+    def makenewdf():
+        """make an empty dataframe, organised in the way we want the target data, ready to be populated"""
+        validfactions = ['witches', 'auren', 'swarmlings', 'mermaids', 'cultists', 'halflings', 'dwarves', 'engineers', 'chaosmagicians', 'giants', 'fakirs', 'nomads', 'darklings', 'alchemists']
+        dfcols = ['game'] + validfactions
+        vpdf = pd.DataFrame(columns=dfcols)
+
+        return vpdf, dfcols, validfactions
+
+    vpdf, dfcols, validfactions = makenewdf()
+
+    def get_vp_from_game(singleGameEvents):
+        """Input game events for a single game. This is a pd.DataFrame.
+        Output a row where each faction in the game has its vp populated (the rest are nans)
+        """
+        newdf = pd.DataFrame([[np.nan] * 15], columns=dfcols)
+
+        # assign the game number
+        gameno = list(pd.unique(singleGameEvents['game']))
+
+        # assert len(gameno) == 1, 'More than 1 unique game was found'
+        try:
+            newdf['game'].replace({np.nan: gameno[0]}, inplace=True)
+        except:
+            print(f'DEBUGGING: len of table is {len(singleGameEvents)}')
+            print(f'DEBUGGING: gamnos list: {gameno}')
+            print(singleGameEvents)
+            raise
+
+        # find factions - there are some artifacts in the data. E.g. the "faction", "all". We need to filter them out.
+        rawfactions = list(pd.unique(singleGameEvents['faction']))
+        verifiedfactions = [rawfaction for rawfaction in rawfactions if rawfaction in validfactions]
+
+        for faction in verifiedfactions:
+            vpfaction = sum(singleGameEvents[(singleGameEvents['event'] == 'vp') & (singleGameEvents['faction'] == faction)]['num'])
+            newdf[faction].replace({np.nan: vpfaction}, inplace=True)
+
+        return newdf
+
+    # two features dataset functions
+    def emptyfeaturesdf():
+        """make an empty dataframe, organised in the way we want the feature data, ready to be populated"""
+        colnames = ['game']
+        uniqueScoreTiles = np.sort(pd.unique(gamescoringtiles['tile']))
+
+        # One-hot of round tiles, for each round
+        for gameround in range(1, 7):
+            roundstr = f'r{gameround}'
+            for tile in uniqueScoreTiles:
+                colnames.append(roundstr + '_' + tile)
+
+        # Boolean of bonus tiles
+        for bon in range(1, 11):
+            colnames.append(f'BON{bon}')
+
+        # One-hot player count (from 2, 3, 4 or 5 players)
+        for player in range(2, 6):
+            colnames.append(f'{player}players')
+
+        # one hot of the map used
+        """126fe960806d587c78546b30f1a90853b1ada468 - map1
+           95a66999127893f5925a5f591d54f8bcb9a670e6 - map2
+           be8f6ebf549404d015547152d5f2a1906ae8dd90 - map3
+        """
+        colnames = colnames + ['map1', 'map2', 'map3']
+
+        featuresdf = pd.DataFrame(columns=colnames)
+
+        return featuresdf, colnames
+
+    featuresdf, featcolnames = emptyfeaturesdf()
+
+    def get_features_from_game(singlegameevents, singlegamemeta, singlegameST, singleendplayers=None):
+        """
+        Inputs:
+            singlegameevents <pd.DataFrame>   - is game events for a single game
+            singlegamemeta   <pd.DataFrame>   - is a single row from `games` that gives map & player count
+            singlegameST     <pd.DataFrame>   - is a single row from `gamescoringtiles` that gives... score tile (suprisingly)
+            singleendplayers <pd.DataFrame>   - is a single row from `end players` that gives the amount of players at end of game, after dropouts
+        Return:          <pd.DataFrame>   - a row where features have been found (will be sparse)
+        """
+        newdf = pd.DataFrame([[0] * len(featcolnames)], columns=featcolnames)
+
+        # assign game string
+        singlegamemeta.iloc[0]['game']
+        newdf['game'].replace({0: singlegamemeta.iloc[0]['game']}, inplace=True)
+
+        # find the round tiles for each round
+        for gameround in range(1, 7):
+            roundstr = f'r{gameround}'
+            scoretile = roundstr + '_' + singlegameST[singlegameST['round'] == gameround]['tile'].values[0]
+            newdf[scoretile].replace({0: 1}, inplace=True)
+
+        # Boolean of bonus tiles
+        uniqueevents = list(pd.unique(singlegameevents['event']))
+        bonustiles = [event[5:] for event in uniqueevents if event.startswith('pass:BON')]
+        for bontile in bonustiles:
+            newdf[bontile].replace({0: 1}, inplace=True)
+
+        # One-hot player count (from 2, 3, 4 or 5 players)
+        if singleendplayers is None:
+            noplayers = singlegamemeta.iloc[0]['player_count']
+            print('gamemeta used for player count')
+        else:
+            noplayers = singleendplayers.iloc[0]['endplayers']
+
+        players = f'{noplayers}players'
+        newdf[players].replace({0: 1}, inplace=True)
+
+        # one hot of the map used
+        mapdict = {'126fe960806d587c78546b30f1a90853b1ada468': 'map1',
+                   '95a66999127893f5925a5f591d54f8bcb9a670e6': 'map2',
+                   'be8f6ebf549404d015547152d5f2a1906ae8dd90': 'map3'
+                   }
+        basemap = singlegamemeta.iloc[0]['base_map']
+        gamemap = mapdict[basemap]
+        newdf[gamemap].replace({0: 1}, inplace=True)
+
+        return newdf
+
+    # filtering
+    # making a dataset for ease
+    data = dict()
+    data['gameevents'] = gameevents
+    data['games'] = games
+    data['gamescoringtiles'] = gamescoringtiles
+
+    def filteringByBadgames(data, badgames):
+        """ Data is a dict containing gameevents, games, gamescoringtiles
+            badgames is a pd.dataframe that contains ['game'] to filter by
+        """
+        gameeventsfil = data['gameevents']
+        gamesfil = data['games']
+        gamescoringtilesfil = data['gamescoringtiles']
+
+        badgameslist = badgames['game']
+        gameeventsfilbefore = len(gameeventsfil)
+        gamesbefore = len(gamesfil)
+        gameSTbefore = len(gamescoringtilesfil)
+
+        gameeventsfil = gameeventsfil[~gameeventsfil['game'].isin(badgameslist)]
+        gamesfil = gamesfil[~gamesfil['game'].isin(badgameslist)]
+        gamescoringtilesfil = gamescoringtilesfil[~gamescoringtilesfil['game'].isin(badgameslist)]
+
+        print(f'game events before: {gameeventsfilbefore}, game events after: {len(gameeventsfil)}, game events removed: {gameeventsfilbefore-len(gameeventsfil)}')
+        print(f'games before: {gamesbefore}, games after: {len(gamesfil)}, games removed: {gamesbefore-len(gamesfil)}')
+        print(f'gameST before: {gameSTbefore}, gameST after: {len(gamescoringtilesfil)}, games removed: {gameSTbefore-len(gamescoringtilesfil)}')
+
+        data['gameevents'] = gameeventsfil
+        data['games'] = gamesfil
+        data['gamescoringtiles'] = gamescoringtilesfil
+
+        return data
+
+    # player count
+    badgames = games[games["player_count"].isin([1, 6, 7])]
+    data = filteringByBadgames(data, badgames)
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/faction-picker-bot/.ipynb_checkpoints/shap_metrics-checkpoint.ipynb b/faction-picker-bot/.ipynb_checkpoints/shap_metrics-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..363fcab7ed6e9634e198cf5555ceb88932c9a245
--- /dev/null
+++ b/faction-picker-bot/.ipynb_checkpoints/shap_metrics-checkpoint.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/faction-picker-bot/.ipynb_checkpoints/testdvc-checkpoint.ipynb b/faction-picker-bot/.ipynb_checkpoints/testdvc-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..e355424585c743c090172aa0b1413d75237c3885
--- /dev/null
+++ b/faction-picker-bot/.ipynb_checkpoints/testdvc-checkpoint.ipynb
@@ -0,0 +1,44 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "test = pd.DataFrame([[1, 2]], columns=['col1', 'col2'])\n",
+    "test.to_csv('D://PycharmProjects/TerraBot/data/faction-picker-bot/testdvc.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/faction-picker-bot/.ipynb_checkpoints/train_model-checkpoint.py b/faction-picker-bot/.ipynb_checkpoints/train_model-checkpoint.py
new file mode 100644
index 0000000000000000000000000000000000000000..02abae478b06c23184228ebb49521acb2a84c4df
--- /dev/null
+++ b/faction-picker-bot/.ipynb_checkpoints/train_model-checkpoint.py
@@ -0,0 +1,9 @@
+import pandas as pd
+import numpy as np
+import math
+import time
+
+vpdf = pd.read_csv('D://PycharmProjects/TerraBot/data/faction-picker-bot/vpdata.csv')
+featdf = pd.read_csv('/data/faction-picker-bot/featdata-backup.csv')
+
+# split in 12 faction data
diff --git a/faction-picker-bot/.ipynb_checkpoints/train_model_notebook-checkpoint.ipynb b/faction-picker-bot/.ipynb_checkpoints/train_model_notebook-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..6ba6418f4af69dcd8de5d1b1882a04cef9b9d7ce
--- /dev/null
+++ b/faction-picker-bot/.ipynb_checkpoints/train_model_notebook-checkpoint.ipynb
@@ -0,0 +1,446 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "3d683865-b1cc-4826-973b-f31eb87d15d6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import yaml\n",
+    "import pickle\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import lightgbm as lgb\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "efc65ac4-fcbf-4fb7-8251-353017129972",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('..\\params.yaml', 'r') as fd:\n",
+    "    params = yaml.safe_load(fd)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "3a782515-f51f-4cbc-bb43-6ae00cb73a1f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pickledir = params['prepare-step2']['pickle-dir']\n",
+    "with open(pickledir, 'rb') as fd:\n",
+    "    each_faction_dataset = pickle.load(fd)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "828d229a-988f-46fa-a6c9-b1b4c79e0ac4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "feats = each_faction_dataset['auren']['features']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c48ee433-73ec-479c-a539-2bbd0ac28da1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_data = np.array(each_faction_dataset['auren']['vp'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "e49647c2-3e2c-4813-a439-211a31ad5547",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "numpy.ndarray"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(y_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "58631132-ccb0-4b0d-91af-1c5ed98b15c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(11610,)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "id": "8eb2d842-f641-4154-b685-5140547461c2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "faction = 'swarmlings'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "id": "d1586415-2be1-4a00-acd8-7e29c6ac05db",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Xdata = each_faction_dataset[faction]['features']\n",
+    "Xdata.drop(['Unnamed: 0', 'game'], axis=1)\n",
+    "traindata = lgb.Dataset(Xdata,\n",
+    "                        label=np.array(each_faction_dataset[faction]['vp']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "id": "5071ec0c-8728-43a4-93aa-76584c6a397a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(27967, 71)"
+      ]
+     },
+     "execution_count": 139,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Xdata = each_faction_dataset[faction]['features']\n",
+    "Xdata = Xdata.drop(['Unnamed: 0', 'game'], axis=1)\n",
+    "Xdata.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "id": "de0efbdb-1e7b-4f45-b0c3-fab4f81a39cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['r1_SCORE1', 'r1_SCORE2', 'r1_SCORE3', 'r1_SCORE4', 'r1_SCORE5',\n",
+       "       'r1_SCORE6', 'r1_SCORE7', 'r1_SCORE8', 'r1_SCORE9', 'r2_SCORE1',\n",
+       "       'r2_SCORE2', 'r2_SCORE3', 'r2_SCORE4', 'r2_SCORE5', 'r2_SCORE6',\n",
+       "       'r2_SCORE7', 'r2_SCORE8', 'r2_SCORE9', 'r3_SCORE1', 'r3_SCORE2',\n",
+       "       'r3_SCORE3', 'r3_SCORE4', 'r3_SCORE5', 'r3_SCORE6', 'r3_SCORE7',\n",
+       "       'r3_SCORE8', 'r3_SCORE9', 'r4_SCORE1', 'r4_SCORE2', 'r4_SCORE3',\n",
+       "       'r4_SCORE4', 'r4_SCORE5', 'r4_SCORE6', 'r4_SCORE7', 'r4_SCORE8',\n",
+       "       'r4_SCORE9', 'r5_SCORE1', 'r5_SCORE2', 'r5_SCORE3', 'r5_SCORE4',\n",
+       "       'r5_SCORE5', 'r5_SCORE6', 'r5_SCORE7', 'r5_SCORE8', 'r5_SCORE9',\n",
+       "       'r6_SCORE1', 'r6_SCORE2', 'r6_SCORE3', 'r6_SCORE4', 'r6_SCORE5',\n",
+       "       'r6_SCORE6', 'r6_SCORE7', 'r6_SCORE8', 'r6_SCORE9', 'BON1', 'BON2',\n",
+       "       'BON3', 'BON4', 'BON5', 'BON6', 'BON7', 'BON8', 'BON9', 'BON10',\n",
+       "       '2players', '3players', '4players', '5players', 'map1', 'map2', 'map3'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 140,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Xdata.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "id": "2be0344e-fed9-4948-ad08-09a149e2879c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "traindata = lgb.Dataset(Xdata,\n",
+    "                        label=np.array(each_faction_dataset[faction]['vp']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "id": "fe7a3155-d267-46f8-a43b-3f3118ae9603",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "27967"
+      ]
+     },
+     "execution_count": 142,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(np.array(each_faction_dataset[faction]['vp']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "id": "f09074d1-9b69-4dd7-856c-5442216a60e2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.004396 seconds.\n",
+      "You can set `force_row_wise=true` to remove the overhead.\n",
+      "And if memory is not enough, you can set `force_col_wise=true`.\n",
+      "[LightGBM] [Info] Total Bins 138\n",
+      "[LightGBM] [Info] Number of data points in the train set: 27967, number of used features: 69\n",
+      "[LightGBM] [Info] Start training from score 101.014803\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_round=500\n",
+    "param = {'num_leaves': 300, 'objective': 'regression'}\n",
+    "bst = lgb.train(param, traindata, num_round)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "id": "86325e1c-1589-446a-a228-54b3a25f707f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(27967, 71)"
+      ]
+     },
+     "execution_count": 144,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "testdata = Xdata.iloc[:, :]\n",
+    "testdata.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "id": "292895d5-cf79-453f-942f-37aea70e3f3f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ypred = bst.predict(Xdata)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "id": "2d81210a-c6a7-4c2e-b7b8-d482769f9675",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import metrics\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "id": "ffd227ba-1852-473c-8e9a-c781b0bf908d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5.046741950738864"
+      ]
+     },
+     "execution_count": 147,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "MAE = metrics.mean_absolute_error(ypred, np.array(each_faction_dataset[faction]['vp']))\n",
+    "MAE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "id": "c4fd6197-784b-41f6-bd79-4ad30efb5df8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "line = list(range(250))\n",
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot(111)\n",
+    "ax.set_aspect('equal', adjustable='box')\n",
+    "ax.set_title(f'{faction} residuals')\n",
+    "plt.plot(ypred, np.array(each_faction_dataset[faction]['vp']), 'bo', line, line, 'r--')\n",
+    "plt.xlabel('y_pred')\n",
+    "plt.ylabel('y_real')\n",
+    "plt.xlim([0, 250])\n",
+    "plt.ylim([0, 250])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "id": "466b46dc-fadd-4290-9eca-7c9d3a3d3b99",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "residuals = np.array(ypred) - np.array(each_faction_dataset[faction]['vp'])\n",
+    "avgres = np.mean(residuals)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "id": "c73d5a71-188e-4b18-bd3c-b47cb3650d0a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot(111)\n",
+    "ax.set_title(f'{faction} residuals histogram')\n",
+    "plt.hist(residuals, bins=100)\n",
+    "plt.xlabel('Difference +/- of y_pred relative to y_real')\n",
+    "h = ax.plot([avgres, avgres], [0, 600], 'r--')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "id": "a19a9388-36da-4c6f-bf5c-6216431b9c02",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, (ax1, ax2) = plt.subplots(1, 2)\n",
+    "ax1.set_aspect('equal', adjustable='box')\n",
+    "ax1.set_title(f'{faction} residuals')\n",
+    "ax1.plot(ypred, np.array(each_faction_dataset[faction]['vp']), 'bo', line, line, 'r--')\n",
+    "ax1.set(xlabel='y_pred', ylabel='y_real')\n",
+    "ax1.set(xlim=[0, 250], ylim=[0, 250])\n",
+    "\n",
+    "ax2.set_title(f'{faction} residuals histogram')\n",
+    "ax2.hist(residuals, bins=100)\n",
+    "ax2.set(xlabel='Difference +/- of y_pred relative to y_real')\n",
+    "h = ax2.plot([avgres, avgres], [0, 600], 'r--')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "id": "dc99b122-0c67-4af8-9c7d-22230cf32166",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metricsdir = 'data/faction-picker-bot/metrics/'\n",
+    "plt.savefig(metricsdir + f'{faction} charts.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2e65f854-b42e-485c-9af9-179e951c1f42",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/faction-picker-bot/.ipynb_checkpoints/train_models-checkpoint.py b/faction-picker-bot/.ipynb_checkpoints/train_models-checkpoint.py
new file mode 100644
index 0000000000000000000000000000000000000000..6bd26f4bbccba887c8fcb6640443d3788ae2e549
--- /dev/null
+++ b/faction-picker-bot/.ipynb_checkpoints/train_models-checkpoint.py
@@ -0,0 +1,32 @@
+import pandas as pd
+import numpy as np
+import lightgbm as lgb
+import argparse
+import pickle
+import yaml
+import math
+import time
+
+
+def main(pickledir):
+    with open(pickledir, 'rb') as fd:
+        each_faction_dataset = pickle.load(fd)
+
+    for faction in each_faction_dataset.keys():
+        X_data = lgb.Dataset(each_faction_dataset[faction]['features'])
+        y_data = each_faction_dataset[faction]['vp']
+
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Input DVC params.')
+    parser.add_argument('--params', type=str)
+    args = parser.parse_args()
+    paramsdir = args.params
+
+    with open(paramsdir, 'r') as fd:
+        params = yaml.safe_load(fd)
+
+    pickledir = params['prepare-step2']['pickle-dir']
+
+    main(pickledir)
diff --git a/faction-picker-bot/__pycache__/make_data.cpython-37.pyc b/faction-picker-bot/__pycache__/make_data.cpython-37.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..f9149a55a3bb48908570dd78036566423b008ec9
Binary files /dev/null and b/faction-picker-bot/__pycache__/make_data.cpython-37.pyc differ
diff --git a/faction-picker-bot/create_individual_datasets.py b/faction-picker-bot/create_individual_datasets.py
new file mode 100644
index 0000000000000000000000000000000000000000..a23f62154451f20c380664ba32c7c2f8bfde7331
--- /dev/null
+++ b/faction-picker-bot/create_individual_datasets.py
@@ -0,0 +1,93 @@
+import pandas as pd
+import argparse
+import pickle
+import yaml
+from sklearn.preprocessing import LabelEncoder
+from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder
+import numpy as np
+
+
+def featurise_features(featdf, params):
+    # adjust features dataset for chosen encoding
+    game = featdf.iloc[:, :1]
+    rounddata = featdf.iloc[:, 1:7]
+    bontiles = featdf.iloc[:, 7:17]
+    playerdata = featdf.iloc[:, 17:18]
+    colours = featdf.iloc[:, 18:25]
+    mapdata = featdf.iloc[:, -1:]
+
+    onehot_encoder = OneHotEncoder(sparse=False)
+    ordinal_encoder = OrdinalEncoder()
+
+    if params['prepare-step2']['round-features'] == 'ordinal':
+        rounddatanp = ordinal_encoder.fit_transform(rounddata)
+        rounddata = pd.DataFrame(data=rounddatanp, columns=rounddata.columns)
+    else:  # one-hot
+        rounddatanp = onehot_encoder.fit_transform(rounddata)
+        rounddata = pd.DataFrame(data=rounddatanp, columns=onehot_encoder.get_feature_names())
+    if params['prepare-step2']['playercount-features'] == 'ordinal':
+        playerdatanp = ordinal_encoder.fit_transform(playerdata)
+        playerdata = pd.DataFrame(data=playerdatanp, columns=playerdata.columns)
+    else:  # one-hot
+        playerdatanp = onehot_encoder.fit_transform(playerdata)
+        playerdata = pd.DataFrame(data=playerdatanp, columns=onehot_encoder.get_feature_names())
+    if params['prepare-step2']['map-features'] == 'ordinal':
+        mapdatanp = ordinal_encoder.fit_transform(mapdata)
+        mapdata = pd.DataFrame(data=mapdatanp, columns=mapdata.columns)
+    else:  # one-hot
+        mapdatanp = onehot_encoder.fit_transform(mapdata)
+        mapdata = pd.DataFrame(data=mapdatanp, columns=onehot_encoder.get_feature_names())
+
+    featdf = pd.concat([game, rounddata, bontiles, playerdata, colours, mapdata], axis=1)
+    return featdf
+
+
+def main(params):
+    vpdfdir = params['prepare']['vp-data-dir']
+    featdfdir = params['prepare']['feature-data-dir']
+    pickledir = params['prepare-step2']['pickle-dir']
+
+    vpdf = pd.read_csv(vpdfdir)
+    featdf = pd.read_csv(featdfdir)
+
+    vpdf = vpdf.sort_values('game')
+    featdf = featdf.sort_values('game')
+
+    featdf = featdf.drop(columns=['Unnamed: 0'])
+    featdf = featurise_features(featdf, params)
+
+    each_faction_dataset = dict()
+
+    colnames = list(vpdf.columns)
+    factions = [x for x in colnames if x != 'game' and x != 'Unnamed: 0']
+
+    for faction in factions:
+        faction_dataset = {}
+        vpdf = vpdf.sort_index()
+        indexes = pd.isnull(vpdf[faction])
+        vpdata = pd.Series(index=vpdf['game'][~indexes], data=vpdf[faction][~indexes].values)
+
+        featdf = featdf.sort_index()
+        featdata = featdf[~indexes]
+        featdata.index = featdata['game']
+        featdata = featdata.drop(columns=['game'])
+
+        faction_dataset['vp'] = vpdata
+        faction_dataset['features'] = featdata
+        each_faction_dataset[faction] = faction_dataset
+
+    with open(pickledir, 'wb') as pklfile:
+        pickle.dump(each_faction_dataset, pklfile)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Input DVC params.')
+    parser.add_argument('--params', type=str)
+    args = parser.parse_args()
+    paramsdir = args.params
+
+    with open(paramsdir, 'r') as fd:
+        params = yaml.safe_load(fd)
+
+    main(params)
+
diff --git a/faction-picker-bot/create_metrics.py b/faction-picker-bot/create_metrics.py
new file mode 100644
index 0000000000000000000000000000000000000000..fb6e31ba3262d2c6877bf206b3977fa94256bc5a
--- /dev/null
+++ b/faction-picker-bot/create_metrics.py
@@ -0,0 +1,115 @@
+import matplotlib.pyplot as plt
+from pathlib import Path
+import lightgbm as lgb
+import numpy as np
+from sklearn import metrics
+import pandas as pd
+import argparse
+import pickle
+import math
+import json
+import yaml
+import os
+
+
+def main(params):
+    pickledir = params['prepare-step2']['pickle-dir']
+    modeldir = params['training']['model-dir']
+    modelmetricsdir = params['training']['model-metrics-dir']
+    metricsdir = params['create-metrics']['metrics-dir']
+    metricsdir2 = params['create-metrics']['metrics-dir2']
+    metricsdir3 = params['create-metrics']['metrics-dir3']
+
+    # get dataset split parameters
+    trainsplit = params['training']['train-proportion']
+    valsplit = params['training']['val-proportion']
+    testsplit = params['training']['test-proportion']
+
+    Path(metricsdir).mkdir(parents=True, exist_ok=True)
+    models = os.listdir(modeldir)
+    model_metrics = dict()
+    model_plot_pd = pd.DataFrame()
+
+    with open(pickledir, 'rb') as fd:
+        each_faction_dataset = pickle.load(fd)
+
+    for model in models:
+        # load model
+        modelfile = modeldir + model
+        faction = model.split('_')[0]
+        bst = lgb.Booster(model_file=modelfile)  # init model
+        evaldict = pickle.load(open(modelmetricsdir + f'{faction}_results.pkl', 'rb'))
+
+        # make the data
+        Xdata = each_faction_dataset[faction]['features']
+        validx = math.ceil(Xdata.shape[0] * (trainsplit + valsplit))
+        testdata = Xdata.iloc[validx:, :]
+        ytest = np.array(each_faction_dataset[faction]['vp'].iloc[validx:])
+
+        # get predictions & residuals
+        ypred = bst.predict(testdata)
+        residuals = np.array(ypred) - ytest
+        avgres = np.mean(residuals)
+        original = np.array(ytest) - np.mean(ytest)
+        MAE = metrics.mean_absolute_error(ypred, ytest)
+        model_metrics[faction] = MAE
+
+        # make plots
+        line = list(range(250))
+        fig, (ax1, ax2, ax3) = plt.subplots(1, 3)
+        fig.set_size_inches(12, 4.8)
+        ax1.set_aspect('equal', adjustable='box')
+        ax1.set_title(f'{faction} residuals')
+        ax1.plot(ypred, ytest, 'bo', line, line, 'r--')
+        ax1.set(xlabel='y_pred', ylabel='y_real')
+        # ax1.xlim([0, 250])
+        # ax1.ylim([0, 250])
+
+        ax2.set_title(f'{faction} residuals histogram')
+        ax2.hist(original, bins=100, color='r')
+        ax2.hist(residuals, bins=100)
+        ax2.set(xlabel='Difference +/- of y_pred relative to y_real')
+        h = ax2.plot([avgres, avgres], [0, 100], 'r--')
+
+        # get data for fig 3
+        if type(evaldict) == list:
+            trainmetrics = []
+            valmetrics = []
+            dictmetric = list(evaldict[0]['train'].keys())[0]
+            for eachevaldict in evaldict:
+                trainmetrics += eachevaldict['train'][dictmetric]
+                valmetrics += eachevaldict['validation'][dictmetric]
+        else:
+            dictmetric = list(evaldict['train'].keys())[0]
+            trainmetrics = evaldict['train'][dictmetric]
+            valmetrics = evaldict['validation'][dictmetric]
+
+        ax3.set_title(f'{faction} training plot')
+        ax3.plot(range(len(trainmetrics)), trainmetrics, 'r', label='train')
+        ax3.plot(range(len(valmetrics)), valmetrics, 'g', label='validation')
+        ax3.set(xlabel='train step', ylabel='l2 loss')
+        ax3.legend()
+        model_plot_pd[faction] = valmetrics
+
+        plt.subplots_adjust(wspace=0.8)
+
+        # save plot
+        plt.savefig(metricsdir + f'/{faction} charts.png')
+
+    with open(metricsdir2, 'w') as fp:
+        json.dump(model_metrics, fp)
+
+    model_plot_pd['step'] = list(range(model_plot_pd.shape[0]))
+    model_plot_pd.to_csv(metricsdir3)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Input DVC params.')
+    parser.add_argument('--params', type=str)
+    args = parser.parse_args()
+    paramsdir = args.params
+
+    with open(paramsdir, 'r') as fd:
+        params = yaml.safe_load(fd)
+
+    main(params)
diff --git a/faction-picker-bot/gradio_interface.py b/faction-picker-bot/gradio_interface.py
new file mode 100644
index 0000000000000000000000000000000000000000..dc8eb8cf478e63565d7fbc485613dc557f2b4b4e
--- /dev/null
+++ b/faction-picker-bot/gradio_interface.py
@@ -0,0 +1,306 @@
+import gradio as gr
+import random
+import matplotlib
+import matplotlib.pyplot as plt
+import pandas as pd
+import shap
+import lightgbm as lgb
+import yaml
+import numpy as np
+import sys
+import os
+
+current_path = os.getcwd() + '\\' + __file__
+vis_path = os.path.dirname(os.path.dirname(current_path)) + '\\visualisations'
+sys.path.append(vis_path)
+import terravisualisation as tmvis
+
+matplotlib.use("Agg")
+
+bontiledict = {'SPD + 2C':'BON1', 
+               'cult + 4C':'BON2', 
+               '+6C':'BON3', 
+               '+3pw 1 ship':'BON4', 
+               '+1W + 3PW':'BON5', 
+               'pass-vp:SA/SH*4 + 2W':'BON6', 
+               'pass-vp:TP*2 + 1W':'BON7', 
+               '+1P':'BON8', 
+               'pass-vp:D*1 + 2C':'BON9', 
+               'pass-vp: ship*3 + 3pw':'BON10' 
+                }
+
+bontiledict_reverse = {'BON1': 'SPD + 2C',
+               'BON2': 'cult + 4C',
+               'BON3': '+6C',
+               'BON4': '+3pw 1 ship',
+               'BON5': '+1W + 3PW',
+               'BON6': 'pass-vp:SA/SH*4 + 2W',
+               'BON7': 'pass-vp:TP*2 + 1W',
+               'BON8': '+1P',
+               'BON9': 'pass-vp:D*1 + 2C',
+               'BON10': 'pass-vp: ship*3 + 3pw'      
+                }
+
+round_tiles_dict = {'SPADE >> 2':'SCORE1',
+                    'TOWN >> 5':'SCORE2',
+                    'D >> 2':'SCORE3',
+                    'SA/SH >> 5':'SCORE4',
+                    'D >> 2':'SCORE5',
+                    'TP >> 3':'SCORE6',
+                    'SA/SH >> 5':'SCORE7',
+                    'TP >> 3':'SCORE8',
+                    'TE >> 4':'SCORE9'}
+
+round_tiles_dict_reverse = {'SCORE1': 'SPADE >> 2',  
+                    'SCORE2': 'TOWN >> 5',  
+                    'SCORE3': 'D >> 2',  
+                    'SCORE4': 'SA/SH >> 5',  
+                    'SCORE5': 'D >> 2',  
+                    'SCORE6': 'TP >> 3',  
+                    'SCORE7': 'SA/SH >> 5',  
+                    'SCORE8': 'TP >> 3',  
+                    'SCORE9': 'TE >> 4'}
+
+
+round_tiles = list(round_tiles_dict.keys())
+bontiles = list(bontiledict.keys())
+
+factions = ['Witches', 'Auren', 'Giants', 'Chaos Magicians', 'Darklings', 'Alchemists',
+            'Swarmlings', 'Mermaids', 'Fakirs', 'Nomads', 'Engineers', 'Dwarves', 'Halflings', 'Cultists']
+
+players = ['2players', '3players', '4players', '5players']
+
+maps = ['map1', 'map2', 'map3']
+
+faction_cols = ['Yellow', 'Red', 'Grey', 'Black', 'Blue', 'Green', 'Brown']
+
+with open('params.yaml', 'r') as fd:
+    params = yaml.safe_load(fd)
+
+vpdfdir = params['prepare']['vp-data-dir']
+featdfdir = params['prepare']['feature-data-dir']
+pickledir = params['prepare-step2']['pickle-dir']
+
+feature_columns = ['x0_SCORE1', 'x0_SCORE2', 'x0_SCORE3', 'x0_SCORE4', 'x0_SCORE5',
+       'x0_SCORE6', 'x0_SCORE7', 'x0_SCORE8', 'x0_SCORE9', 'x1_SCORE1',
+       'x1_SCORE2', 'x1_SCORE3', 'x1_SCORE4', 'x1_SCORE5', 'x1_SCORE6',
+       'x1_SCORE7', 'x1_SCORE8', 'x1_SCORE9', 'x2_SCORE1', 'x2_SCORE2',
+       'x2_SCORE3', 'x2_SCORE4', 'x2_SCORE5', 'x2_SCORE6', 'x2_SCORE7',
+       'x2_SCORE8', 'x2_SCORE9', 'x3_SCORE1', 'x3_SCORE2', 'x3_SCORE3',
+       'x3_SCORE4', 'x3_SCORE5', 'x3_SCORE6', 'x3_SCORE7', 'x3_SCORE8',
+       'x3_SCORE9', 'x4_SCORE1', 'x4_SCORE2', 'x4_SCORE3', 'x4_SCORE4',
+       'x4_SCORE5', 'x4_SCORE6', 'x4_SCORE7', 'x4_SCORE8', 'x4_SCORE9',
+       'x5_SCORE2', 'x5_SCORE3', 'x5_SCORE4', 'x5_SCORE5', 'x5_SCORE6',
+       'x5_SCORE7', 'x5_SCORE8', 'x5_SCORE9', 'BON1', 'BON2', 'BON3', 'BON4',
+       'BON5', 'BON6', 'BON7', 'BON8', 'BON9', 'BON10', 'no_players', 'red',
+       'blue', 'green', 'black', 'grey', 'yellow', 'brown', 'x0_map1',
+       'x0_map2', 'x0_map3']
+
+
+
+def args_to_features(*args):
+    # round1, round2, round3, round4, round5, round6, faction, map, playerschosen, bon_tiles, fac_cols = args
+    Xdata = pd.DataFrame(data=np.zeros((1, len(feature_columns))), columns=feature_columns)
+
+    for arg_no, user_input in enumerate(args):
+        if  arg_no in range(6):  # if it's a round
+            # map back to col name
+            feat_label_name = f'x{arg_no}_{round_tiles_dict[user_input]}'
+            Xdata[feat_label_name].iloc[0] = 1
+        elif arg_no == 6:
+            faction = user_input
+            if faction == 'Chaos Magicians':
+                faction = 'chaosmagicians'
+        elif arg_no == 7: # map 
+            feat_label_name = f'x0_{user_input}'
+            Xdata[feat_label_name].iloc[0] = 1
+        elif arg_no == 8: # playerschosen
+            Xdata['no_players'].iloc[0] = int(user_input[0])
+        elif arg_no == 9: # bon_tiles
+            for bon_tile in user_input:
+                Xdata[bontiledict[bon_tile]].iloc[0] = 1
+        elif arg_no == 9: # fac_cols
+            for fac_col in user_input:
+                Xdata[fac_col.lower()].iloc[0] = 1
+
+    return Xdata, faction
+
+def display_map(faction, map):
+    map_fig = plt.figure(tight_layout=True)
+
+    x, y = tmvis.display_map(faction, plot=False)
+    a = map_fig.add_subplot(111)
+    a.hexbin(x, y, gridsize=(19, 9), cmap='magma')
+    a.axis('off')
+    return map_fig
+
+
+def predict(*args):
+    Xdata, faction = args_to_features(*args)
+
+    modelfile = f'D://PycharmProjects/TerraBot/data/faction-picker-bot/models/{faction}_model.txt'
+    bst = lgb.Booster(model_file=modelfile)
+
+    return f'Final score: {round(bst.predict(Xdata)[0])}'
+
+
+def interpret(*args):
+    Xdata, faction = args_to_features(*args)
+    modelfile = f'D://PycharmProjects/TerraBot/data/faction-picker-bot/models/{faction}_model.txt'
+    bst = lgb.Booster(model_file=modelfile)
+    bst.params["objective"] = "regression"
+    explainer = shap.Explainer(bst)
+
+    copycols = []
+    for ii, column in enumerate(Xdata.columns):
+        if column[-6:] in round_tiles_dict_reverse.keys():
+            copycols.append(column[:3] + round_tiles_dict_reverse[column[-6:]])
+        elif column in bontiledict_reverse.keys():
+            copycols.append(bontiledict_reverse[column])
+        else:
+            copycols.append(column)
+        
+    Xdata.columns = copycols
+
+    shap_values = explainer(Xdata)
+    fig_m = plt.figure(tight_layout=True, facecolor=(0.125,0.172,0.203))
+    ax = plt.gca()
+    ax.set_facecolor((0.125,0.172,0.203))
+    matplotlib.rcParams['axes.labelcolor'] = 'w'
+    shap.plots.waterfall(shap_values[0])
+    # shap.initjs()
+    # shap.plots.force(shap_values[0])
+    return fig_m
+
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("""
+    **Predict final faction score given the initial board setup 💰**:  This model uses an lightgbm regression to make prediction. 
+    The [source code for this work is here](https://github.com/guyreading/terrabot/faction-picker-bot/gradio_interface.py).
+    """)
+    with gr.Row():
+        with gr.Column():
+            faction = gr.Dropdown(
+                label="Faction",
+                choices=factions,
+                value=lambda: random.choice(factions),
+            )
+
+            round1_tile = gr.Dropdown(
+                label="Round 1 tile",
+                choices=round_tiles,
+                value=lambda: random.choice(round_tiles),
+            )
+
+            round2_tile = gr.Dropdown(
+                label="Round 2 tile",
+                choices=round_tiles,
+                value=lambda: random.choice(round_tiles),
+            )
+
+            round3_tile = gr.Dropdown(
+                label="Round 3 tile",
+                choices=round_tiles,
+                value=lambda: random.choice(round_tiles),
+            )
+
+            round4_tile = gr.Dropdown(
+                label="Round 4 tile",
+                choices=round_tiles,
+                value=lambda: random.choice(round_tiles),
+            )
+
+            round5_tile = gr.Dropdown(
+                label="Round 5 tile",
+                choices=round_tiles,
+                value=lambda: random.choice(round_tiles),
+            )
+
+            round6_tile = gr.Dropdown(
+                label="Round 6 tile",
+                choices=round_tiles,
+                value=lambda: random.choice(round_tiles),
+            )
+
+            bon_tiles_gr = gr.CheckboxGroup(label='Bonus tiles present', choices=list(bontiledict.keys()))
+        
+            map = gr.Dropdown(
+                label="Map",
+                choices=maps,
+                value=lambda: random.choice(maps),
+            )
+
+            playerschosen = gr.Dropdown(
+                label="No. Of Players",
+                choices=players,
+                value=lambda: random.choice(players),
+            )
+
+            fac_cols_gr = gr.CheckboxGroup(label='Other faction colours present', choices=faction_cols)
+
+
+        with gr.Column():
+            map_plot = gr.Plot(label='Distance from home terrain: darker is further')
+
+            with gr.Row():
+                predict_btn = gr.Button(value="Predict")
+                interpret_btn = gr.Button(value="Explain")
+
+            label = gr.Label(label=f'Prediction of final VP for faction:')
+            plot = gr.Plot(label=f'Breakdown of prediction for faction:')
+
+    predict_btn.click(
+        predict,
+        inputs=[
+            round1_tile,
+            round2_tile,
+            round3_tile,
+            round4_tile,
+            round5_tile,
+            round6_tile,
+            faction,
+            map,
+            playerschosen,
+            bon_tiles_gr,
+            fac_cols_gr
+        ],
+        outputs=[label],
+    )
+    interpret_btn.click(
+        interpret,
+        inputs=[
+            round1_tile,
+            round2_tile,
+            round3_tile,
+            round4_tile,
+            round5_tile,
+            round6_tile,
+            faction,
+            map,
+            playerschosen,
+            bon_tiles_gr,
+            fac_cols_gr
+        ],
+        outputs=[plot],
+    )
+
+    faction.change(
+        display_map,
+        inputs=[
+            faction,
+            map,
+        ],
+        outputs=[map_plot],
+    )
+
+    map.change(
+        display_map,
+        inputs=[
+            faction,
+            map,
+        ],
+        outputs=[map_plot],
+    )
+
+demo.launch()
\ No newline at end of file
diff --git a/faction-picker-bot/make_data.py b/faction-picker-bot/make_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..11e09e28d6593f33413ba247bfc2764221b3a360
--- /dev/null
+++ b/faction-picker-bot/make_data.py
@@ -0,0 +1,316 @@
+import pandas as pd
+import numpy as np
+import argparse
+import math
+import time
+import yaml
+
+
+def main(folderlocation, vpdfdir, featdfdir, playerdropdir):
+    """This is the script version of creatingVPdata.ipynb for the dvc pipeline."""
+    # load data in
+    gameevents = pd.read_csv(f'{folderlocation}game_events.csv')
+    games = pd.read_csv(f'{folderlocation}games.csv')
+    gameslist = list(pd.unique(gameevents['game']))
+    allfactions = pd.unique(gameevents['faction'])
+    gamescoringtiles = pd.read_csv(f'{folderlocation}game_scoring_tiles.csv')
+    gameoptions = pd.read_csv(f'{folderlocation}game_options.csv')
+    stats = pd.read_csv(f'{folderlocation}stats.csv')
+
+    # two vp dataset functions
+    def makenewdf():
+        """make an empty dataframe, organised in the way we want the target data, ready to be populated"""
+        validfactions = ['witches', 'auren', 'swarmlings', 'mermaids', 'cultists', 'halflings', 'dwarves', 'engineers', 'chaosmagicians', 'giants', 'fakirs', 'nomads', 'darklings', 'alchemists']
+        dfcols = ['game'] + validfactions
+        vpdf = pd.DataFrame(columns=dfcols)
+
+        return vpdf, dfcols, validfactions
+
+    vpdf, dfcols, validfactions = makenewdf()
+
+    def get_vp_from_game(singleGameEvents):
+        """Input game events for a single game. This is a pd.DataFrame.
+        Output a row where each faction in the game has its vp populated (the rest are nans)
+        """
+        newdf = pd.DataFrame([[np.nan] * 15], columns=dfcols)
+
+        # assign the game number
+        gameno = list(pd.unique(singleGameEvents['game']))
+
+        # assert len(gameno) == 1, 'More than 1 unique game was found'
+        try:
+            newdf['game'].replace({np.nan: gameno[0]}, inplace=True)
+        except:
+            print(f'DEBUGGING: len of table is {len(singleGameEvents)}')
+            print(f'DEBUGGING: gamnos list: {gameno}')
+            print(singleGameEvents)
+            raise
+
+        # find factions - there are some artifacts in the data. E.g. the "faction", "all". We need to filter them out.
+        rawfactions = list(pd.unique(singleGameEvents['faction']))
+        verifiedfactions = [rawfaction for rawfaction in rawfactions if rawfaction in validfactions]
+
+        for faction in verifiedfactions:
+            vpfaction = sum(singleGameEvents[(singleGameEvents['event'] == 'vp') & (singleGameEvents['faction'] == faction)]['num'])
+            newdf[faction].replace({np.nan: vpfaction}, inplace=True)
+
+        return newdf
+
+    # two features dataset functions
+    def emptyfeaturesdf():
+        """make an empty dataframe, organised in the way we want the feature data, ready to be populated"""
+        colnames = ['game']
+        uniqueScoreTiles = np.sort(pd.unique(gamescoringtiles['tile']))
+
+        # One-hot of round tiles, for each round
+        for gameround in range(1, 7):
+            colnames.append(f'round{gameround}tile')
+
+
+        # Boolean of bonus tiles
+        for bon in range(1, 11):
+            colnames.append(f'BON{bon}')
+
+        # One-hot player count
+        colnames.append('no_players')
+
+        # Boolean of other colours present on the board
+        colnames = colnames + ['red', 'blue', 'green', 'black', 'grey', 'yellow', 'brown']
+
+
+        # acceptable maps
+        """126fe960806d587c78546b30f1a90853b1ada468 - map1
+           95a66999127893f5925a5f591d54f8bcb9a670e6 - map2
+           be8f6ebf549404d015547152d5f2a1906ae8dd90 - map3
+        """
+        colnames.append('map')
+
+        featuresdf = pd.DataFrame(columns=colnames)
+
+        return featuresdf, colnames
+
+    featuresdf, featcolnames = emptyfeaturesdf()
+
+    def get_features_from_game(singlegameevents, singlegamemeta, singlegameST, singleendplayers=None):
+        """
+        Inputs:
+            singlegameevents <pd.DataFrame>   - is game events for a single game
+            singlegamemeta   <pd.DataFrame>   - is a single row from `games` that gives map & player count
+            singlegameST     <pd.DataFrame>   - is a single row from `gamescoringtiles` that gives... score tile (suprisingly)
+            singleendplayers <pd.DataFrame>   - is a single row from `end players` that gives the amount of players at end of game, after dropouts
+        Return:          <pd.DataFrame>   - a row where features have been found (will be sparse)
+        """
+        newdf = pd.DataFrame([[0] * len(featcolnames)], columns=featcolnames)
+
+        # assign game string
+        newdf['game'].replace({0: singlegamemeta.iloc[0]['game']}, inplace=True)
+
+        # find the round tiles for each round
+        for gameround in range(1, 7):
+            roundstr = f'round{gameround}tile'
+            scoretile = singlegameST[singlegameST['round'] == gameround]['tile'].values[0]
+            newdf[roundstr].replace({0: scoretile}, inplace=True)
+
+        # Boolean of bonus tiles
+        uniqueevents = list(pd.unique(singlegameevents['event']))
+        bonustiles = [event[5:] for event in uniqueevents if event.startswith('pass:BON')]
+        for bontile in bonustiles:
+            newdf[bontile].replace({0: 1}, inplace=True)
+
+        # get faction colour
+        factions = pd.unique(singlegameevents['faction'])
+        if 'auren' in factions or 'witches' in factions:
+            newdf['green'].replace({0: 1}, inplace=True)
+        if 'swarmlings' in factions or 'mermaids' in factions:
+            newdf['blue'].replace({0: 1}, inplace=True)
+        if 'cultists' in factions or 'halflings' in factions:
+            newdf['brown'].replace({0: 1}, inplace=True)
+        if 'darklings' in factions or 'alchemists' in factions:
+            newdf['black'].replace({0: 1}, inplace=True)
+        if 'nomads' in factions or 'fakirs' in factions:
+            newdf['yellow'].replace({0: 1}, inplace=True)
+        if 'giants' in factions or 'chaosmagicians' in factions:
+            newdf['red'].replace({0: 1}, inplace=True)
+        if 'engineers' in factions or 'dwarves' in factions:
+            newdf['grey'].replace({0: 1}, inplace=True)
+
+        # Number player count (from 2, 3, 4 or 5 players)
+        if singleendplayers is None:
+            noplayers = singlegamemeta.iloc[0]['player_count']
+            print('gamemeta used for player count')
+        else:
+            noplayers = singleendplayers.iloc[0]['endplayers']
+
+        newdf['no_players'].replace({0: noplayers}, inplace=True)
+
+        # one hot of the map used
+        mapdict = {'126fe960806d587c78546b30f1a90853b1ada468': 'map1',
+                   '95a66999127893f5925a5f591d54f8bcb9a670e6': 'map2',
+                   'be8f6ebf549404d015547152d5f2a1906ae8dd90': 'map3'
+                   }
+        basemap = singlegamemeta.iloc[0]['base_map']
+        gamemap = mapdict[basemap]
+        newdf['map'].replace({0: gamemap}, inplace=True)
+
+        return newdf
+
+    # filtering
+    # making a dataset for ease
+    data = dict()
+    data['gameevents'] = gameevents
+    data['games'] = games
+    data['gamescoringtiles'] = gamescoringtiles
+
+    def filteringByBadgames(data, badgames):
+        """ Data is a dict containing gameevents, games, gamescoringtiles
+            badgames is a pd.dataframe that contains ['game'] to filter by
+        """
+        gameeventsfil = data['gameevents']
+        gamesfil = data['games']
+        gamescoringtilesfil = data['gamescoringtiles']
+
+        badgameslist = badgames['game']
+        gameeventsfilbefore = len(gameeventsfil)
+        gamesbefore = len(gamesfil)
+        gameSTbefore = len(gamescoringtilesfil)
+
+        gameeventsfil = gameeventsfil[~gameeventsfil['game'].isin(badgameslist)]
+        gamesfil = gamesfil[~gamesfil['game'].isin(badgameslist)]
+        gamescoringtilesfil = gamescoringtilesfil[~gamescoringtilesfil['game'].isin(badgameslist)]
+
+        print(f'game events before: {gameeventsfilbefore}, game events after: {len(gameeventsfil)}, game events removed: {gameeventsfilbefore-len(gameeventsfil)}')
+        print(f'games before: {gamesbefore}, games after: {len(gamesfil)}, games removed: {gamesbefore-len(gamesfil)}')
+        print(f'gameST before: {gameSTbefore}, gameST after: {len(gamescoringtilesfil)}, games removed: {gameSTbefore-len(gamescoringtilesfil)}')
+
+        data['gameevents'] = gameeventsfil
+        data['games'] = gamesfil
+        data['gamescoringtiles'] = gamescoringtilesfil
+
+        return data
+
+    # player count
+    badgames = games[games["player_count"].isin([1, 6, 7])]
+    data = filteringByBadgames(data, badgames)
+
+    extendedfactions = validfactions + ['dragonlords', 'riverwalkers', 'yetis', 'icemaidens', 'shapeshifters', 'acolytes']
+
+    def check_game_ended(singlegame, verbose=False):
+        r5 = singlegame[singlegame['round'] == 5]
+        rawfactions = pd.unique(singlegame['faction'])
+        verifiedfactions = [rawfaction for rawfaction in rawfactions if rawfaction in extendedfactions]
+        allbool = []
+
+        for faction in verifiedfactions:
+            factionbool = len(r5[(r5['faction'] == faction)  & (r5['event'].str.startswith('pass'))]) == 1
+            allbool.append(factionbool)
+
+            if verbose:
+                print(f'faction: {faction} ended their turn?: {factionbool}')
+
+        isgood = all(allbool)
+        startplayers = len(verifiedfactions)
+        boolsum = sum(allbool)
+        playersdropped = startplayers - boolsum
+
+        return isgood, startplayers, playersdropped
+
+    gameevents = data['gameevents']
+    gameslist = list(pd.unique(gameevents['game']))
+
+    gamelengthlen = len(gameslist)
+    gamesroundup = math.ceil(gamelengthlen / 100.0) * 100
+    jj = 0
+    playerdropdf = pd.DataFrame(columns=['game', 'nodrops', 'startplayers', 'playersdropped'])
+
+    for ii in range(100, gamesroundup+1, 100):
+        ii = min(ii, gamelengthlen)
+        if (ii % 10000) == 0:  # update every 10000 games
+            print(f'Progressed to {ii}th game')
+
+        next100games = gameslist[jj:ii]
+        jj = ii  # so that we don't get any repetitions at the very end, where our set will be smaller
+
+        gameevents100 = gameevents[gameevents['game'].isin(next100games)]
+
+        for game in next100games:
+            singlegame = gameevents100[gameevents100['game'] == game]
+
+            if not len(singlegame) == 0:
+                isgood, startplayers, playersdropped = check_game_ended(singlegame)
+                newdf = pd.DataFrame([[game, isgood, startplayers, playersdropped]], columns=['game', 'nodrops', 'startplayers', 'playersdropped'])
+                playerdropdf = playerdropdf.append(newdf, ignore_index=True)
+
+    playerdropdf['endplayers'] = playerdropdf['startplayers'] - playerdropdf['playersdropped']
+    playerdropdf.to_csv(playerdropdir)
+
+    badgames = playerdropdf[playerdropdf['endplayers'].isin([0, 1])]
+    data = filteringByBadgames(data, badgames)
+
+    # removing unwanted maps
+    acceptablemaps = ['126fe960806d587c78546b30f1a90853b1ada468',
+                      '95a66999127893f5925a5f591d54f8bcb9a670e6',
+                      'be8f6ebf549404d015547152d5f2a1906ae8dd90']
+
+    badgames = games[~games["base_map"].isin(acceptablemaps)]
+    data = filteringByBadgames(data, badgames)
+
+    # creating full dataset
+    vpdf, _, _ = makenewdf()
+    featdf, _ = emptyfeaturesdf()
+
+    gameevents = data['gameevents']
+    games = data['games']
+    gamescoringtiles = data['gamescoringtiles']
+
+    gameslist = list(pd.unique(gameevents['game']))
+    gamelengthlen = len(gameslist)
+    gamesroundup = math.ceil(gamelengthlen / 100.0) * 100
+    jj = 0
+
+    for ii in range(100, gamesroundup+1, 100):
+        ii = min(ii, gamelengthlen)
+        if (ii % 10000) == 0:  # update every 10000 games
+            print(f'Progressed to {ii}th game')
+
+        next100games = gameslist[jj:ii]
+        jj = ii  # so that we don't get any repetitions at the very end, where our set will be smaller
+
+        gameevents100 = gameevents[gameevents['game'].isin(next100games)]
+        gamemeta100 = games[games['game'].isin(next100games)]
+        gameST100 = gamescoringtiles[gamescoringtiles['game'].isin(next100games)]
+        endplayers100 = playerdropdf[playerdropdf['game'].isin(next100games)]  # use this for player count
+
+        for game in next100games:
+            singlegame = gameevents100[gameevents100['game'] == game]
+            singlegamemeta = gamemeta100[gamemeta100['game'] == game]
+            singlegameST = gameST100[gameST100['game'] == game]
+            singleendplayers = endplayers100[endplayers100['game'] == game]
+
+            if not len(singlegame) == 0:
+                vpforgame = get_vp_from_game(singlegame)
+                featsforgame = get_features_from_game(singlegame, singlegamemeta, singlegameST, singleendplayers=singleendplayers)
+
+                vpdf = vpdf.append(vpforgame, ignore_index=True)
+                featdf = featdf.append(featsforgame, ignore_index=True)
+
+    print(f"no of unique games in table is: {len(list(pd.unique(vpdf['game'])))}")
+
+    vpdf.to_csv(vpdfdir)
+    featdf.to_csv(featdfdir)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Input DVC params.')
+    parser.add_argument('--params', type=str)
+    args = parser.parse_args()
+    paramsdir = args.params
+
+    with open(paramsdir, 'r') as fd:
+        params = yaml.safe_load(fd)
+
+    vpdfdir = params['prepare']['vp-data-dir']
+    featdfdir = params['prepare']['feature-data-dir']
+    playerdropdir = params['prepare']['player-drop-dir']
+    folderlocation = params['prepare']['folderlocation']
+
+    main(folderlocation, vpdfdir, featdfdir, playerdropdir)
diff --git a/faction-picker-bot/shap_metrics.py b/faction-picker-bot/shap_metrics.py
new file mode 100644
index 0000000000000000000000000000000000000000..b390bf4f72be222007698c589c037e6e94a2bbda
--- /dev/null
+++ b/faction-picker-bot/shap_metrics.py
@@ -0,0 +1,49 @@
+import lightgbm as lgb
+from pathlib import Path
+import argparse
+import pickle
+import yaml
+import shap
+import os
+
+
+def main(pickledir, modeldir, shapdir):
+    Path(shapdir).mkdir(parents=True, exist_ok=True)
+    models = os.listdir(modeldir)
+
+    with open(pickledir, 'rb') as fd:
+        each_faction_dataset = pickle.load(fd)
+
+    for model in models:
+        # load model
+        modelfile = modeldir + model
+        faction = model.split('_')[0]
+        bst = lgb.Booster(model_file=modelfile)  # init model
+        bst.params["objective"] = "regression"
+
+        # load data
+        Xdata = each_faction_dataset[faction]['features']
+        Xdata = Xdata.drop(['Unnamed: 0', 'game'], axis=1)
+
+        explainer = shap.Explainer(bst)
+        shap_values = explainer(Xdata)
+
+        factionshap = shapdir + faction + '_shap.pkl'
+        with open(factionshap, 'w') as fp:
+            pickle.dump(shap_values, fp)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Input DVC params.')
+    parser.add_argument('--params', type=str)
+    args = parser.parse_args()
+    paramsdir = args.params
+
+    with open(paramsdir, 'r') as fd:
+        params = yaml.safe_load(fd)
+
+    pickledir = params['prepare-step2']['pickle-dir']
+    modeldir = params['training']['model-dir']
+    shapdir = params['shap-metrics']['shap-dir']
+
+    main(pickledir, modeldir, shapdir)
\ No newline at end of file
diff --git a/faction-picker-bot/train_models.py b/faction-picker-bot/train_models.py
new file mode 100644
index 0000000000000000000000000000000000000000..08c38c9b8878f2c22e557158cbd0df042283d6c7
--- /dev/null
+++ b/faction-picker-bot/train_models.py
@@ -0,0 +1,182 @@
+from pathlib import Path
+import numpy as np
+import lightgbm as lgb
+import argparse
+import pickle
+import yaml
+import math
+
+import tensorflow as tf
+from tensorflow import keras
+# from tensorflow.keras import layers
+# import tensorflow_datasets as tfds
+# import tensorflow_probability as tfp
+
+
+def main(params):
+    pickledir = params['prepare-step2']['pickle-dir']
+    modeldir = params['training']['model-dir']
+    modelmetricsdir = params['training']['model-metrics-dir']
+
+    Path(modeldir).mkdir(parents=True, exist_ok=True)
+    Path(modelmetricsdir).mkdir(parents=True, exist_ok=True)
+
+    with open(pickledir, 'rb') as fd:
+        each_faction_dataset = pickle.load(fd)
+
+    # get model parameters
+    num_round = params['training']['num-rounds']
+
+    # get dataset split parameters
+    trainsplit = params['training']['train-proportion']
+    valsplit = params['training']['val-proportion']
+    testsplit = params['training']['test-proportion']
+    assert trainsplit + valsplit + testsplit == 1, "dataset train/val/test split != 1"
+
+    for faction in each_faction_dataset.keys():
+        print(faction)
+
+        # make the data
+        Xdata = each_faction_dataset[faction]['features']
+        trainidx = math.ceil(Xdata.shape[0] * trainsplit)
+        validx = math.ceil(Xdata.shape[0] * (trainsplit + valsplit))
+        traindata = Xdata.iloc[:trainidx, :]
+        ytrain = np.array(each_faction_dataset[faction]['vp'].iloc[:trainidx])
+        valdata = Xdata.iloc[trainidx:validx, :]
+        yval = np.array(each_faction_dataset[faction]['vp'].iloc[trainidx:validx])
+        # testdata = Xdata.iloc[validx:, :]
+        # ytest = np.array(each_faction_dataset[faction]['vp'].iloc[validx:])
+
+        # train model - this is running one of the training scripts below
+        training_routine = params['training']['training-routine'] + "(traindata, ytrain, valdata, yval, num_round)"
+        model, evaldict = eval(training_routine)
+
+        # save model
+        model.save_model(modeldir + f'/{faction}_model.txt')
+
+        # save eval results
+        pickle.dump(evaldict, open(modelmetricsdir + f'{faction}_results.pkl', 'wb'))
+
+
+def lgb_train_method(traindata, ytrain, valdata, yval, num_round):
+    """Train a LightGBM from the train script"""
+    traindataset = lgb.Dataset(traindata, label=ytrain)
+    valdataset = lgb.Dataset(valdata, label=yval)
+
+    model_kwargs = params['training']['lgbt-model-kwargs']
+
+    # train model
+    evaldict = {}
+    model = lgb.train(model_kwargs,
+                      traindataset,
+                      num_round,
+                      valid_sets=[valdataset, traindataset],
+                      valid_names=['validation', 'train'],
+                      early_stopping_rounds=10,
+                      evals_result=evaldict,
+                      verbose_eval=False
+                      )
+
+    return model, evaldict
+
+
+def lgb_kfolds_scikitlearn(traindata, ytrain, valdata, yval, num_round):
+    """Train a LightGBM with the scikitlearn API + some kfolds"""
+    increment = round(len(traindata) * 0.29)
+    data_idx = increment
+    split_rounds = params['training']['split-rounds']
+    modelkwargs = params['training']['lgbt-model-kwargs']
+    evaldictslist = []
+
+    model = lgb.LGBMRegressor(
+        n_estimators=num_round,
+        **modelkwargs
+    )
+
+    for roundno in range(split_rounds):
+        evaldict = {}
+        evalcallback = lgb.record_evaluation(evaldict)
+        start = data_idx % len(traindata)
+        data_idx += increment
+        end = data_idx % len(traindata)
+        if end < start:
+            xsubset = np.vstack((traindata.iloc[start:, :], traindata.iloc[:end, :]))
+            ysubset = np.concatenate((ytrain[start:], ytrain[:end]))
+        else:
+            xsubset = traindata.iloc[start:end, :]
+            ysubset = ytrain[start:end]
+
+        model.fit(
+            xsubset, ysubset,
+            eval_set=[(valdata, yval), (traindata, ytrain)],
+            eval_names=['validation', 'train'],
+            verbose=False,
+            callbacks=[evalcallback],
+        )
+
+        evaldictslist.append(evaldict)
+
+    model = model.booster_
+
+    return model, evaldictslist
+
+
+def nn_train_method(traindata, ytrain, valdata, yval, num_round):
+    # initialising hyperparameters
+    model_kwargs = params['training']['nn-model-kwargs']
+    batch_size = 256
+    num_epochs = 100
+    
+    # create inputs
+    inputs = {}
+    for feature_name in traindata.columns:
+        inputs[feature_name] = layers.Input(
+            name=feature_name, shape=(1,), dtype=tf.float32
+        )
+    
+    # create model
+    input_values = [value for _, value in sorted(inputs.items())]
+    features = keras.layers.concatenate(input_values)
+    features = layers.BatchNormalization()(features)
+
+    # Create hidden layers with deterministic weights using the Dense layer.
+    for units in model_kwargs['hidden_units']:
+        features = layers.Dense(units, activation="sigmoid")(features)
+    # The output is deterministic: a single point estimate.
+    outputs = layers.Dense(units=1)(features)
+
+    model = keras.Model(inputs=inputs, outputs=outputs)
+
+    # train
+    if model_kwargs['loss'] == 'mse':
+        loss = keras.losses.MeanSquaredError()
+    else:
+        raise('Unsupported loss present')
+
+    model.compile(
+        optimizer=keras.optimizers.RMSprop(learning_rate=model_kwargs['learning_rate']),
+        loss=loss,
+        metrics=[keras.metrics.RootMeanSquaredError()],
+    )
+
+    print("Start training the model...")
+    model.fit(train_dataset, epochs=model_kwargs['num_epochs'], validation_data=test_dataset)
+    print("Model training finished.")
+    _, rmse = model.evaluate(train_dataset, verbose=0)
+    print(f"Train RMSE: {round(rmse, 3)}")
+
+    print("Evaluating model performance...")
+    _, rmse = model.evaluate(test_dataset, verbose=0)
+    print(f"Test RMSE: {round(rmse, 3)}")
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Input DVC params.')
+    parser.add_argument('--params', type=str)
+    args = parser.parse_args()
+    paramsdir = args.params
+
+    with open(paramsdir, 'r') as fd:
+        params = yaml.safe_load(fd)
+
+    main(params)
diff --git a/visualisations/.ipynb_checkpoints/terravisgui-checkpoint.py b/visualisations/.ipynb_checkpoints/terravisgui-checkpoint.py
new file mode 100644
index 0000000000000000000000000000000000000000..42e90679cc04b42f4d1aa88c4041c8f08b6064c7
--- /dev/null
+++ b/visualisations/.ipynb_checkpoints/terravisgui-checkpoint.py
@@ -0,0 +1,69 @@
+"""Creates a GUI containing a dropdown and the terra map. The user selects the faction from the
+drop down & the map displays tiles according to distance from that faction's home times.
+"""
+
+import tkinter
+import terravisualisation as tmvis
+from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
+
+# Implement the default Matplotlib key bindings.
+from matplotlib.figure import Figure
+
+root = tkinter.Tk()
+root.wm_title("Faction map")
+
+# add the photo
+root.iconbitmap('D:/PycharmProjects/TerraBot/images/tmbotico.ico')
+
+# make the dropdown
+factions = {'Witches', 'Auren', 'Giants', 'Chaos Magicians', 'Darklings', 'Alchemists',
+            'Swarmlings', 'Mermaids', 'Fakirs', 'Nomads', 'Engineers', 'Dwarves', 'Halflings', 'Cultists'}
+
+tkvar = tkinter.StringVar(root)
+tkvar.set('Witches')
+
+popupMenu = tkinter.OptionMenu(root, tkvar, *factions)
+popupMenu.pack(side=tkinter.TOP)
+
+# make the figure
+fig = Figure(figsize=(5, 4), dpi=100)
+a = fig.add_subplot(111)
+canvas = FigureCanvasTkAgg(fig, master=root)  # A tk.DrawingArea.
+
+
+def createtheplot(faction):
+    x, y = tmvis.display_map(faction, plot=False)
+
+    a.hexbin(x, y, gridsize=(19, 9), cmap='magma')
+    a.axis('off')
+
+    canvas.draw()
+    canvas.get_tk_widget().pack(side=tkinter.TOP, fill=tkinter.BOTH, expand=1)
+
+
+createtheplot(tkvar.get())
+
+
+# on change dropdown value
+def change_dropdown(*args):
+    createtheplot(tkvar.get())
+
+
+# link function to change dropdown
+tkvar.trace('w', change_dropdown)
+
+
+# make the quit button
+def _quit():
+    root.quit()     # stops mainloop
+    root.destroy()  # this is necessary on Windows to prevent
+                    # Fatal Python Error: PyEval_RestoreThread: NULL tstate
+
+
+button = tkinter.Button(master=root, text="Quit", command=_quit)
+button.pack(side=tkinter.BOTTOM)
+
+tkinter.mainloop()
+# If you put root.destroy() here, it will cause an error if the window is
+# closed with the window manager
+
diff --git a/visualisations/terravisgui.py b/visualisations/terravisgui.py
new file mode 100644
index 0000000000000000000000000000000000000000..42e90679cc04b42f4d1aa88c4041c8f08b6064c7
--- /dev/null
+++ b/visualisations/terravisgui.py
@@ -0,0 +1,69 @@
+"""Creates a GUI containing a dropdown and the terra map. The user selects the faction from the
+drop down & the map displays tiles according to distance from that faction's home times.
+"""
+
+import tkinter
+import terravisualisation as tmvis
+from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
+
+# Implement the default Matplotlib key bindings.
+from matplotlib.figure import Figure
+
+root = tkinter.Tk()
+root.wm_title("Faction map")
+
+# add the photo
+root.iconbitmap('D:/PycharmProjects/TerraBot/images/tmbotico.ico')
+
+# make the dropdown
+factions = {'Witches', 'Auren', 'Giants', 'Chaos Magicians', 'Darklings', 'Alchemists',
+            'Swarmlings', 'Mermaids', 'Fakirs', 'Nomads', 'Engineers', 'Dwarves', 'Halflings', 'Cultists'}
+
+tkvar = tkinter.StringVar(root)
+tkvar.set('Witches')
+
+popupMenu = tkinter.OptionMenu(root, tkvar, *factions)
+popupMenu.pack(side=tkinter.TOP)
+
+# make the figure
+fig = Figure(figsize=(5, 4), dpi=100)
+a = fig.add_subplot(111)
+canvas = FigureCanvasTkAgg(fig, master=root)  # A tk.DrawingArea.
+
+
+def createtheplot(faction):
+    x, y = tmvis.display_map(faction, plot=False)
+
+    a.hexbin(x, y, gridsize=(19, 9), cmap='magma')
+    a.axis('off')
+
+    canvas.draw()
+    canvas.get_tk_widget().pack(side=tkinter.TOP, fill=tkinter.BOTH, expand=1)
+
+
+createtheplot(tkvar.get())
+
+
+# on change dropdown value
+def change_dropdown(*args):
+    createtheplot(tkvar.get())
+
+
+# link function to change dropdown
+tkvar.trace('w', change_dropdown)
+
+
+# make the quit button
+def _quit():
+    root.quit()     # stops mainloop
+    root.destroy()  # this is necessary on Windows to prevent
+                    # Fatal Python Error: PyEval_RestoreThread: NULL tstate
+
+
+button = tkinter.Button(master=root, text="Quit", command=_quit)
+button.pack(side=tkinter.BOTTOM)
+
+tkinter.mainloop()
+# If you put root.destroy() here, it will cause an error if the window is
+# closed with the window manager
+
diff --git a/visualisations/terravisualisation.py b/visualisations/terravisualisation.py
new file mode 100644
index 0000000000000000000000000000000000000000..2ee6fb705d1bc12e47f39b89203c8dec7a35c23f
--- /dev/null
+++ b/visualisations/terravisualisation.py
@@ -0,0 +1,152 @@
+"""terravisualisation contains all the data processing & matplotlib graphing functions related to board visualisation
+functions for terraBot. However for an interaction GUI, this function is contained in "terravisgui". This GUI uses
+the tools built in terravisualisation.
+"""
+
+import numpy as np
+
+
+def rotate_around_dig_wheel(gremangles, rotation):
+    from math import pi
+
+    # rotate
+    blumangles = (gremangles + ((rotation / 7) * pi) + pi) % (2 * pi) - pi
+
+    # back to numbers
+    bluemap = blumangles / (pi * (2 / 7))
+
+    # create the array
+    rotatedmap = 4 - abs(bluemap)
+
+    return rotatedmap
+
+
+def faction_map(faction):
+    """faction_map outputs an array representing the Terra Mystica map, where they array
+    shows the amount of digs every tile is away from a faction's home tile
+
+    Arguments:
+    faction -- a string referring to a TM faction
+
+    Returns:
+    finalarr -- a (9, 13) array where each element is [4 - the amount of digs a faction is away
+    from their home tile], where an element maps to a tile. Rivers have a value of 0. Rows of the
+    map constituting of 12 tiles have their final element set to 0, representing a blank tile
+     """
+    from math import pi
+
+    # for green
+    greenmap = np.array([[-3, 1, 0, -1, 3, 2, -3, -2, 2, 0, -1, 2, -2],
+                         [3, 4, 4, -3, -2, 4, 4, 3, -2, 4, 4, 3, 4],
+                         [4, 4, -2, 4, 1, 4, 0, 4, 0, 4, 1, 4, 4],
+                         [0, -1, 3, 4, 4, 2, -1, 4, 2, 4, 2, -3, 4],
+                         [-2, -3, 2, -1, -2, -3, 1, 3, 4, 4, 0, -2, -1],
+                         [1, 0, 4, 4, 3, 0, 4, 4, 4, -3, 1, -3, 4],
+                         [4, 4, 4, 1, 4, 2, 4, 0, 4, 3, -2, -1, 3],
+                         [3, -1, -3, 4, 4, 4, -1, -2, 4, 1, -3, 1, 4],
+                         [2, -2, 1, -1, 2, 0, 3, -3, 1, 4, -1, 0, 2]])
+
+    gremangles = greenmap * (pi * (2/7))
+        
+    # river & empties mask
+    rivermask = np.array([[False, False, False, False, False, False, False, False, False, False, False, False, False],
+                          [False, True, True, False, False, True, True, False, False, True, True, False, True],
+                          [True, True, False, True, False, True, False, True, False, True, False, True, True],
+                          [False, False, False, True, True, False, False, True, False, True, False, False, True],
+                          [False, False, False, False, False, False, False, False, True, True, False, False, False],
+                          [False, False, True, True, False, False, True,  True,  True, False, False, False, True],
+                          [True, True, True, False, True, False, True, False, True, False, False, False, False],
+                          [False, False, False, True, True, True, False, False, True, False, False, False, True],
+                          [False, False, False, False, False, False, False, False, False, True, False, False, False]])
+
+    if faction == 'witches' or faction == 'auren':
+        # create the array
+        arr1 = 4 - abs(greenmap)
+
+    elif faction == 'swarmlings' or faction == 'mermaids':
+        arr1 = rotate_around_dig_wheel(gremangles, 2)
+
+    elif faction == 'darklings' or faction == 'alchemists':
+        arr1 = rotate_around_dig_wheel(gremangles, 4)
+
+    elif faction == 'halflings' or faction == 'cultists':
+        arr1 = rotate_around_dig_wheel(gremangles, 6)
+
+    elif faction == 'engineers' or faction == 'dwarves':
+        arr1 = rotate_around_dig_wheel(gremangles, -2)
+
+    elif faction == 'chaos magicians' or faction == 'giants':
+        arr1 = rotate_around_dig_wheel(gremangles, -4)
+
+    elif faction == 'fakirs' or faction == 'nomads':
+        arr1 = rotate_around_dig_wheel(gremangles, -6)
+
+    else:
+        return
+
+    # apply river spacing mask over
+    # alt: arr1 = np.multiply(arr1, 1 - rivermask)
+    arr1[rivermask] = 0
+
+    # turn back to integer from potential float
+    arr1 = arr1.astype(int)
+
+    # flip
+    arr2 = np.flip(arr1, 0)
+
+    # re-size
+    arr3 = np.squeeze(np.resize(arr2, (1, 117)))
+
+    # remove the added hexes
+    finalarr = np.delete(arr3, [25, 51, 77, 103])
+
+    return finalarr
+
+
+def display_map(faction, plot=True):
+    """takes the input, faction, and returns a map of the board
+    where hex brightness relates to how many digs that faction
+    needs to convert that hex into its home territory.
+
+    Arguments:
+    faction -- string or (9, 13) numpy array: where the string refers to a faction name,
+    or the numpy array refers to map to be plotted in the format of faction_map
+
+    Returns:
+    None. Plots the map.
+    """
+    import matplotlib.pyplot as plt
+    import numpy.matlib
+
+    # make sure the faction string is all lowercase
+    faction = faction.lower()
+
+    # create array to display map on
+    x1 = np.linspace(4.5, 16.5, 13)
+    x2 = np.linspace(5, 16, 12)
+    x3 = np.hstack((x1, x2))
+    x4 = np.matlib.repmat(x3, 1, 4)
+    x4 = np.squeeze(x4)
+    x5 = np.hstack((x4, x1))
+    x = np.squeeze(x5)
+
+    y1 = np.linspace(6, 15, 9)
+    y = np.repeat(y1, np.array([13, 12, 13, 12, 13, 12, 13, 12, 13]))
+
+    if type(faction) == str:
+        factionmap = faction_map(faction)
+    else:  # assume it's already a map mask array
+        factionmap = faction
+
+    x = np.repeat(x, factionmap)
+    y = np.repeat(y, factionmap)
+
+    # need to define the size of the plot
+    x = np.hstack((x, [1, 1, 20, 20]))
+    y = np.hstack((y, [1, 20, 1, 20]))
+
+    if plot:
+        plt.hexbin(x, y, gridsize=(19, 9), cmap='magma')
+        plt.show()
+    else:
+        return x, y