Upload 12 files

.gitattributes

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+logs.log filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,164 @@
+import streamlit as st
+from streamlit_option_menu import option_menu
+from pandas_profiling import ProfileReport
+from streamlit_pandas_profiling import st_profile_report
+import os 
+import pandas as pd
+from pycaret.classification import *
+from classification import prep_and_train
+import time
+from get_profile import get_profile
+from classification import tuning
+
+# if os.path.exists('./dataset.csv'): 
+#     df = pd.read_csv('dataset.csv', index_col=None)
+
+s = ClassificationExperiment()
+
+
+with st.sidebar: #Side bar config
+    selected = option_menu(menu_title=None,options=["Home", 'Classification','Regression', 'Time Series'], 
+        icons=['house', 'file-binary','graph-up','bezier2'], menu_icon="cast", default_index=0)
+    st.title("Upload Your Dataset")
+    file = st.file_uploader("Upload Your Dataset")
+    if file: 
+        st.session_state.df = pd.read_csv(file, index_col=None)
+        st.session_state.df.to_csv('dataset.csv', index=None)
+
+
+if selected == 'Home':
+    nones = ['None' for i in range(5)]
+    section= option_menu(None, ["Info", "Data profile",'AutoML'], 
+    default_index=0, icons=nones,orientation="horizontal")
+    if section== 'Info':
+        st.title('Main info about service')
+        st.write('Some Text about service')
+    if section == 'Data profile':
+        st.title('This section will give you main information about uploaded dataset')
+        st.write('Simply click "Generate new profile" if you want to generate new profile data and click "View old report to load previous profile"')
+        if st.checkbox('Huge Dataset'):
+            speedup = 'config_minimal.yaml'
+        else:
+            speedup= 'config_default.yaml'
+        if st.button('Generate report'):
+            try:
+                st_profile_report(get_profile(st.session_state.df, speedup))
+            except NameError:
+                st.error('Please upload dataset first')
+
+if selected == 'Classification':
+    section = option_menu(None, ["Prep & Train",'Tune & Analyse','Predict'], 
+    default_index=0,icons=['1-square','1-square','1-square'],orientation="horizontal")
+    if section == 'Prep & Train':
+        col1, col2 = st.columns([3,1.6])
+        with col2:
+            try:
+                    st.title("Prepare you data and train best model")
+                    st.session_state.targ = st.selectbox('Choose target', st.session_state.df.columns)
+                    # time = st.slider('budget_time', 0.3, 1.5, 0.5, 0.1)
+                    dic ={
+                        'lr':'LogReg',
+                        'ridge':'Ridge Classifier',
+                        'lda':'Linear Discriminant Analysis',
+                        'et':'Extra Trees Classifier',
+                        'nb':'Naive Bayes',
+                        'qda':'Quadratic Discriminant Analysis',
+                        'rf':'Random Forest Classifier',
+                        'gbc':'Gradient Boosting Classifier',
+                        'lightgbm':'Light Gradient Boosting Machine',
+                        'catboost':'CatBoost Classifier',
+                        'ada':'Ada Boost Classifier',
+                        'dt':'Decision Tree Classifier',
+                        'knn':'K Neighbors Classifier',
+                        'dummy':'Dummy Classifier',
+                        'svm':'SVM - Linear Kernel'
+                    }
+                    model = st.multiselect('Choose model',
+                                        ['lr',
+                                            'ridge',
+                                            'lda',
+                                            'et',
+                                            'nb',
+                                            'qda',
+                                            'rf',
+                                            'gbc',
+                                            'lightgbm',
+                                            'catboost',
+                                            'ada',
+                                            'dt',
+                                            'knn',
+                                            'dummy',
+                                            'svm'], help='Blablabla', format_func=lambda x: dic.get(x)) 
+                    if st.button('Try model'):
+                        try:
+                            st.session_state.best, st.session_state.model_info, st.session_state.metrics_info = prep_and_train(st.session_state.targ, st.session_state.df, model)
+                            save_model(st.session_state.best, 'dt_pipeline')
+                            # model_info.to_csv('model_info.csv', index=None)
+                            # metrics_info.to_csv('metrics_info.csv',index=None)
+                            with col1:
+                                st.subheader('Actual Model')
+                                st.session_state.model_info_last = st.session_state.model_info
+                                st.session_state.metrics_info_last = st.session_state.metrics_info
+                                col1, col2 = st.columns([3.5,1.8])
+                                with col1:
+                                    st.dataframe(st.session_state.metrics_info)
+                                with col2:
+                                    st.dataframe(st.session_state.model_info)     
+                        except ValueError:
+                                st.error('Please choose target with binary labels')
+                    else:
+                            try:
+                                with col1:
+                                    st.subheader('Your last teached model')
+                                    col1, col2 = st.columns([3.5,1.8])
+                                    with col1:
+                                        st.dataframe(st.session_state.metrics_info_last)
+                                    with col2:
+                                        st.dataframe(st.session_state.model_info_last)
+                            except AttributeError:
+                                st.write('teach the first model')
+
+            except AttributeError: 
+                st.error('Please load dataset first')
+
+    if section == 'Tune & Analyse':
+        st.title('Choose parameters to tune your model')
+        metrics_info_last = pd.read_csv('metrics_info.csv', index_col=None)
+        st.subheader('Current model')
+        st.table(st.session_state.metrics_info_last.head(1))
+        col1,col2,col3 = st.columns(3)
+        with col1:
+            plot_model(st.session_state.best, plot = 'auc', display_format='streamlit')
+        with col2:
+            plot_model(st.session_state.best, plot = 'threshold', display_format='streamlit')
+        with col3:
+            plot_model(st.session_state.best, plot = 'confusion_matrix', display_format='streamlit')
+
+        col1, col2 = st.columns([2,4])
+        with col2:
+            option = st.selectbox(
+            'Choose the tuning engine',
+            ('scikit-learn', 'optuna', 'scikit-optimize'))
+            st.session_state.optimize = st.selectbox('Choose metric to optimize', ('Accuracy','AUC','F1'))
+            st.session_state.iters = st.slider('n_estimators', 5, 20, 5, 1)  
+            if st.button('Tune'):
+                clf1 = setup(data = st.session_state.df, target = st.session_state.targ)
+                st.session_state.tuned_dt = tune_model(estimator=st.session_state.best,n_iter=st.session_state.iters,choose_better=True,optimize=st.session_state.optimize)
+                st.session_state.info_df = pull()
+            with col1:
+                try:
+                    st.dataframe(st.session_state.info_df)
+                    st.write('Last best params')
+                    st.write(st.session_state.tuned_dt)
+                except AttributeError:
+                    pass
+        
+
+
+
+
+            
+
+
+            
+

automl.py

@@ -0,0 +1 @@
+from pycaret.classification import *

classification.py

@@ -0,0 +1,20 @@
+from pycaret.classification import *
+import streamlit as st
+
+@st.cache_data
+def prep_and_train(targ, data, models=None):
+    s = setup(data, target = targ, session_id = 12)
+    s_df = pull()
+    best = compare_models(include=models)
+    best_df = pull()
+    return best, s_df, best_df
+
+
+@st.cache_resource
+def tuning(_model, n_iters, search_lib):
+    tuned_dt = tune_model(estimator=_model, n_iter=n_iters, search_library=search_lib, choose_better=True)
+    info_df = pull()
+    return tuned_dt, info_df
+
+
+

config_default.yaml

@@ -0,0 +1,217 @@
+# Title of the document
+title: "Pandas Profiling Report"
+
+# Metadata
+dataset:
+  description: ""
+  creator: ""
+  author: ""
+  copyright_holder: ""
+  copyright_year: ""
+  url: ""
+
+variables:
+  descriptions: {}
+
+# infer dtypes
+infer_dtypes: true
+
+# Show the description at each variable (in addition to the overview tab)
+show_variable_description: true
+
+# Number of workers (0=multiprocessing.cpu_count())
+pool_size: 0
+
+# Show the progress bar
+progress_bar: true
+
+# Per variable type description settings
+vars:
+    num:
+        quantiles:
+              - 0.05
+              - 0.25
+              - 0.5
+              - 0.75
+              - 0.95
+        skewness_threshold: 20
+        low_categorical_threshold: 5
+        # Set to zero to disable
+        chi_squared_threshold: 0.999
+    cat:
+        length: true
+        characters: true
+        words: true
+        cardinality_threshold: 50
+        n_obs: 5
+        # Set to zero to disable
+        chi_squared_threshold: 0.999
+        coerce_str_to_date: false
+        redact: false
+        histogram_largest: 50
+        stop_words: []
+    bool:
+        n_obs: 3
+        # string to boolean mapping dict
+        mappings:
+            t: true
+            f: false
+            yes: true
+            no: false
+            y: true
+            n: false
+            true: true
+            false: false
+    file:
+        active: false
+    image:
+        active: false
+        exif: true
+        hash: true
+    path:
+        active: false
+    url:
+        active: false
+    timeseries:
+        active: false
+        autocorrelation: 0.7
+        lags: [1, 7, 12, 24, 30]
+        significance: 0.05
+        pacf_acf_lag: 100
+
+# Sort the variables. Possible values: "ascending", "descending" or null (leaves original sorting)
+sort: null
+
+# which diagrams to show
+missing_diagrams:
+    bar: true
+    matrix: true
+    heatmap: true
+
+correlations:
+    pearson:
+      calculate: false
+      warn_high_correlations: true
+      threshold: 0.9
+    spearman:
+      calculate: false
+      warn_high_correlations: false
+      threshold: 0.9
+    kendall:
+      calculate: false
+      warn_high_correlations: false
+      threshold: 0.9
+    phi_k:
+      calculate: false
+      warn_high_correlations: false
+      threshold: 0.9
+    cramers:
+      calculate: false
+      warn_high_correlations: true
+      threshold: 0.9
+    auto:
+      calculate: true
+      warn_high_correlations: true
+      threshold: 0.9
+
+
+# Bivariate / Pairwise relations
+interactions:
+  targets: []
+  continuous: true
+
+# For categorical
+categorical_maximum_correlation_distinct: 100
+
+report:
+  precision: 10
+
+# Plot-specific settings
+plot:
+    # Image format (svg or png)
+    image_format: "svg"
+    dpi: 800
+
+    scatter_threshold: 1000
+
+    correlation:
+        cmap: 'RdBu'
+        bad: '#000000'
+
+    missing:
+        cmap: 'RdBu'
+        # Force labels when there are > 50 variables
+        # https://github.com/ResidentMario/missingno/issues/93#issuecomment-513322615
+        force_labels: true
+
+    cat_frequency:
+        show: true  # if false, the category frequency plot is turned off
+        type: 'bar' # options: 'bar', 'pie'
+        max_unique: 10
+        colors: null # use null for default or give a list of matplotlib recognised strings      
+
+    histogram:
+        x_axis_labels: true
+
+        # Number of bins (set to 0 to automatically detect the bin size)
+        bins: 50
+
+        # Maximum number of bins (when bins=0)
+        max_bins: 250
+
+# The number of observations to show
+n_obs_unique: 5
+n_extreme_obs: 5
+n_freq_table_max: 10
+
+# Use `deep` flag for memory_usage
+memory_deep: false
+
+# Configuration related to the duplicates
+duplicates:
+    head: 10
+    key: "# duplicates"
+
+# Configuration related to the samples area
+samples:
+    head: 10
+    tail: 10
+    random: 0
+
+# Configuration related to the rejection of variables
+reject_variables: true
+
+# When in a Jupyter notebook
+notebook:
+    iframe:
+        height: '800px'
+        width: '100%'
+        # or 'src'
+        attribute: 'srcdoc'
+
+html:
+    # Minify the html
+    minify_html: true
+
+    # Offline support
+    use_local_assets: true
+
+    # If true, single file, else directory with assets
+    inline: true
+
+    # Show navbar
+    navbar_show: true
+
+    # Assets prefix if inline = true
+    assets_prefix: null
+
+    # Styling options for the HTML report
+    style:
+      theme: null
+      logo: ""
+      primary_colors:
+      - "#ff4b4b"
+      - "#ff4b4b"
+      - "#ff4b4b"
+
+    full_width: false

config_minimal.yaml

@@ -0,0 +1,217 @@
+# Title of the document
+title: "Pandas Profiling Report"
+
+# Metadata
+dataset:
+  description: ""
+  creator: ""
+  author: ""
+  copyright_holder: ""
+  copyright_year: ""
+  url: ""
+
+variables:
+  descriptions: {}
+
+# infer dtypes
+infer_dtypes: false
+
+# Show the description at each variable (in addition to the overview tab)
+show_variable_description: true
+
+# Number of workers (0=multiprocessing.cpu_count())
+pool_size: 0
+
+# Show the progress bar
+progress_bar: true
+
+# Per variable type description settings
+vars:
+    num:
+        quantiles:
+              - 0.05
+              - 0.25
+              - 0.5
+              - 0.75
+              - 0.95
+        skewness_threshold: 20
+        low_categorical_threshold: 5
+        # Set to zero to disable
+        chi_squared_threshold: 0.0
+    cat:
+        length: false
+        characters: false
+        words: false
+        cardinality_threshold: 50
+        n_obs: 5
+        # Set to zero to disable
+        chi_squared_threshold: 0.0
+        coerce_str_to_date: false
+        redact: false
+        histogram_largest: 10
+        stop_words: []
+
+    bool:
+        n_obs: 3
+        # string to boolean mapping dict
+        mappings:
+            t: true
+            f: false
+            yes: true
+            no: false
+            y: true
+            n: false
+            true: true
+            false: false
+    path:
+        active: false
+    file:
+        active: false
+    image:
+        active: false
+        exif: false
+        hash: false
+    url:
+        active: false
+    timeseries:
+        active: false
+        autocorrelation: 0.7
+        lags: [1, 7, 12, 24, 30]
+        significance: 0.05
+        pacf_acf_lag: 100
+
+# Sort the variables. Possible values: "ascending", "descending" or null (leaves original sorting)
+sort: null
+
+# which diagrams to show
+missing_diagrams:
+    bar: false
+    matrix: false
+    heatmap: false
+
+correlations:
+    pearson:
+      calculate: false
+      warn_high_correlations: true
+      threshold: 0.9
+    spearman:
+      calculate: false
+      warn_high_correlations: false
+      threshold: 0.9
+    kendall:
+      calculate: false
+      warn_high_correlations: false
+      threshold: 0.9
+    phi_k:
+      calculate: false
+      warn_high_correlations: false
+      threshold: 0.9
+    cramers:
+      calculate: false
+      warn_high_correlations: true
+      threshold: 0.9
+    auto:
+       calculate: false
+       warn_high_correlations: true
+       threshold: 0.9
+
+
+# Bivariate / Pairwise relations
+interactions:
+  targets: []
+  continuous: false
+
+# For categorical
+categorical_maximum_correlation_distinct: 100
+
+report:
+  precision: 10
+
+# Plot-specific settings
+plot:
+    # Image format (svg or png)
+    image_format: "svg"
+    dpi: 800
+
+    scatter_threshold: 1000
+
+    correlation:
+        cmap: 'RdBu'
+        bad: '#000000'
+
+    missing:
+        cmap: 'RdBu'
+        # Force labels when there are > 50 variables
+        force_labels: true
+
+    cat_frequency:
+        show: true  # if false, the category frequency plot is turned off
+        type: 'bar' # options: 'bar', 'pie'
+        max_unique: 0
+        colors: null # use null for default or give a list of matplotlib recognised strings
+
+    histogram:
+        x_axis_labels: true
+
+        # Number of bins (set to 0 to automatically detect the bin size)
+        bins: 50
+
+        # Maximum number of bins (when bins=0)
+        max_bins: 250
+
+# The number of observations to show
+n_obs_unique: 5
+n_extreme_obs: 5
+n_freq_table_max: 10
+
+# Use `deep` flag for memory_usage
+memory_deep: false
+
+# Configuration related to the duplicates
+duplicates:
+    head: 0
+    key: "# duplicates"
+
+# Configuration related to the samples area
+samples:
+    head: 0
+    tail: 0
+    random: 0
+
+# Configuration related to the rejection of variables
+reject_variables: true
+
+# When in a Jupyter notebook
+notebook:
+    iframe:
+        height: '800px'
+        width: '100%'
+        # or 'src'
+        attribute: 'srcdoc'
+
+html:
+    # Minify the html
+    minify_html: true
+
+    # Offline support
+    use_local_assets: true
+
+    # If true, single file, else directory with assets
+    inline: true
+
+    # Show navbar
+    navbar_show: true
+
+    # Assets prefix if inline = true
+    assets_prefix: null
+
+    # Styling options for the HTML report
+    style:
+      theme: null
+      logo: ""
+      primary_colors:
+      - "#ff4b4b"
+      - "#ff4b4b"
+      - "#ff4b4b"
+
+    full_width: false

dt_pipeline.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f2a7a8683de573281d47244bf3082084758449b707a5d29c9f2f0e59148372d4
+size 1423

get_profile.py

@@ -0,0 +1,8 @@
+from pandas_profiling import ProfileReport
+from streamlit_pandas_profiling import st_profile_report
+import streamlit as st
+
+@st.cache_resource
+def get_profile(data, config):
+    profile_df = ProfileReport(data, config_file=config)
+    return profile_df 

logs.log

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:281d935e21534745dfa0bc192d70b483205199074da960eae51398837b9a5cee
+size 11045508

metrics_info.csv

@@ -0,0 +1,16 @@
+Model,Accuracy,AUC,Recall,Prec.,F1,Kappa,MCC,TT (Sec)
+Logistic Regression,0.8122,0.86,0.6701,0.8088,0.7308,0.5891,0.5969,0.268
+Ridge Classifier,0.8121,0.0,0.6743,0.8075,0.7319,0.5897,0.5977,0.284
+Linear Discriminant Analysis,0.8105,0.86,0.687,0.7978,0.7343,0.5888,0.5964,0.254
+Extra Trees Classifier,0.8025,0.8548,0.6322,0.8124,0.7035,0.5617,0.5755,0.362
+Quadratic Discriminant Analysis,0.732,0.7469,0.4694,0.7386,0.5536,0.3869,0.4118,0.284
+Naive Bayes,0.7303,0.8206,0.4272,0.7762,0.5438,0.3769,0.4135,0.25
+Random Forest Classifier,0.7238,0.853,0.3645,0.8078,0.4889,0.3449,0.3968,0.305
+Light Gradient Boosting Machine,0.7046,0.7528,0.2973,0.8139,0.42,0.2867,0.3499,0.25
+Ada Boost Classifier,0.7031,0.8223,0.3226,0.7584,0.4363,0.2908,0.339,0.3
+Decision Tree Classifier,0.703,0.6303,0.3183,0.7848,0.4332,0.2894,0.3408,0.27
+CatBoost Classifier,0.703,0.8354,0.31,0.7986,0.4337,0.2887,0.3486,0.483
+Gradient Boosting Classifier,0.7014,0.7813,0.2931,0.8077,0.4142,0.2793,0.3421,0.302
+K Neighbors Classifier,0.6485,0.6318,0.4143,0.5685,0.4721,0.2197,0.2294,0.293
+Dummy Classifier,0.6164,0.5,0.0,0.0,0.0,0.0,0.0,0.277
+SVM - Linear Kernel,0.5988,0.0,0.3672,0.6084,0.3292,0.1146,0.1667,0.26

model_info.csv

@@ -0,0 +1,25 @@
+Description,Value
+Session id,12
+Target,Survived
+Target type,Binary
+Original data shape,"(891, 12)"
+Transformed data shape,"(891, 14)"
+Transformed train set shape,"(623, 14)"
+Transformed test set shape,"(268, 14)"
+Ordinal features,1
+Numeric features,6
+Categorical features,5
+Rows with missing values,79.5%
+Preprocess,True
+Imputation type,simple
+Numeric imputation,mean
+Categorical imputation,mode
+Maximum one-hot encoding,25
+Encoding method,
+Fold Generator,StratifiedKFold
+Fold Number,10
+CPU Jobs,-1
+Use GPU,False
+Log Experiment,False
+Experiment Name,clf-default-name
+USI,53c2

requirements.txt

@@ -0,0 +1,431 @@
+aiogram==2.25.1
+aiohttp==3.8.4
+aiosignal==1.3.1
+alabaster @ file:///home/ktietz/src/ci/alabaster_1611921544520/work
+alembic==1.10.3
+altair==4.2.2
+anaconda-client==1.11.0
+anaconda-navigator==2.3.1
+anaconda-project @ file:///opt/conda/conda-bld/anaconda-project_1660339890420/work
+ansi2html==1.8.0
+anyio @ file:///tmp/build/80754af9/anyio_1644463572971/work/dist
+appdirs==1.4.4
+argon2-cffi @ file:///opt/conda/conda-bld/argon2-cffi_1645000214183/work
+argon2-cffi-bindings @ file:///tmp/build/80754af9/argon2-cffi-bindings_1644569679365/work
+arrow @ file:///opt/conda/conda-bld/arrow_1649166651673/work
+astroid @ file:///tmp/abs_e5wkt48jiz/croots/recipe/astroid_1659023120113/work
+astropy @ file:///opt/conda/conda-bld/astropy_1657786094003/work
+async-timeout==4.0.2
+atomicwrites==1.4.0
+attrs @ file:///opt/conda/conda-bld/attrs_1642510447205/work
+Automat @ file:///tmp/build/80754af9/automat_1600298431173/work
+autopep8 @ file:///opt/conda/conda-bld/autopep8_1650463822033/work
+Babel @ file:///tmp/build/80754af9/babel_1620871417480/work
+backcall @ file:///home/ktietz/src/ci/backcall_1611930011877/work
+backports.functools-lru-cache @ file:///tmp/build/80754af9/backports.functools_lru_cache_1618170165463/work
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