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  1. README.md +249 -0
  2. config.json +129 -0
  3. confusion_matrix.png +0 -0
  4. example.pkl +3 -0
README.md ADDED
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+ ---
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+ license: mit
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+ library_name: sklearn
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+ tags:
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+ - sklearn
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+ - skops
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+ - tabular-classification
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+ widget:
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+ structuredData:
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+ Contract:
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+ - Two year
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+ - Month-to-month
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+ - One year
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+ Dependents:
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+ - 'Yes'
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+ - 'No'
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+ - 'No'
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+ DeviceProtection:
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+ - 'No'
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+ - 'No'
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+ - 'Yes'
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+ InternetService:
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+ - Fiber optic
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+ - Fiber optic
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+ - DSL
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+ MonthlyCharges:
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+ - 79.05
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+ - 84.95
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+ - 68.8
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+ MultipleLines:
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+ - 'Yes'
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+ - 'Yes'
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+ - 'Yes'
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+ OnlineBackup:
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+ - 'No'
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+ - 'No'
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+ - 'Yes'
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+ OnlineSecurity:
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+ - 'Yes'
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+ - 'No'
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+ - 'Yes'
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+ PaperlessBilling:
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+ - 'No'
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+ - 'Yes'
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+ - 'No'
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+ Partner:
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+ - 'Yes'
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+ - 'Yes'
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+ - 'No'
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+ PaymentMethod:
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+ - Bank transfer (automatic)
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+ - Electronic check
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+ - Bank transfer (automatic)
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+ PhoneService:
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+ - 'Yes'
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+ - 'Yes'
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+ - 'Yes'
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+ SeniorCitizen:
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+ - 0
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+ - 0
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+ - 0
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+ StreamingMovies:
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+ - 'No'
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+ - 'No'
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+ - 'No'
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+ StreamingTV:
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+ - 'No'
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+ - 'Yes'
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+ - 'No'
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+ TechSupport:
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+ - 'No'
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+ - 'No'
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+ - 'Yes'
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+ TotalCharges:
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+ - 5730.7
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+ - 1378.25
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+ - 4111.35
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+ gender:
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+ - Female
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+ - Female
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+ - Male
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+ tenure:
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+ - 72
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+ - 16
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+ - 63
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+ ---
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+
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+ # Model description
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+
90
+ This is a Logistic Regression model trained on churn dataset.
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+
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+ ## Intended uses & limitations
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+
94
+ This model is not ready to be used in production.
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+
96
+ ## Training Procedure
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+
98
+ ### Hyperparameters
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+
100
+ The model is trained with below hyperparameters.
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+
102
+ <details>
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+ <summary> Click to expand </summary>
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+
105
+ | Hyperparameter | Value |
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+ |--------------------------------------------|-----------------------------------------------------------------------------------|
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+ | memory | |
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+ | steps | [('preprocessor', ColumnTransformer(transformers=[('num',
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+ Pipeline(steps=[('imputer',
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+ SimpleImputer(strategy='median')),
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+ ('std_scaler',
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+ StandardScaler())]),
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+ ['MonthlyCharges', 'TotalCharges', 'tenure']),
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+ ('cat', OneHotEncoder(),
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+ ['SeniorCitizen', 'gender', 'Partner',
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+ 'Dependents', 'PhoneService', 'MultipleLines',
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+ 'InternetService', 'OnlineSecurity',
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+ 'OnlineBackup', 'DeviceProtection',
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+ 'TechSupport', 'StreamingTV',
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+ 'StreamingMovies', 'Contract',
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+ 'PaperlessBilling', 'PaymentMethod'])])), ('classifier', LogisticRegression(class_weight='balanced', max_iter=300))] |
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+ | verbose | False |
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+ | preprocessor | ColumnTransformer(transformers=[('num',
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+ Pipeline(steps=[('imputer',
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+ SimpleImputer(strategy='median')),
126
+ ('std_scaler',
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+ StandardScaler())]),
128
+ ['MonthlyCharges', 'TotalCharges', 'tenure']),
129
+ ('cat', OneHotEncoder(),
130
+ ['SeniorCitizen', 'gender', 'Partner',
131
+ 'Dependents', 'PhoneService', 'MultipleLines',
132
+ 'InternetService', 'OnlineSecurity',
133
+ 'OnlineBackup', 'DeviceProtection',
134
+ 'TechSupport', 'StreamingTV',
135
+ 'StreamingMovies', 'Contract',
136
+ 'PaperlessBilling', 'PaymentMethod'])]) |
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+ | classifier | LogisticRegression(class_weight='balanced', max_iter=300) |
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+ | preprocessor__n_jobs | |
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+ | preprocessor__remainder | drop |
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+ | preprocessor__sparse_threshold | 0.3 |
141
+ | preprocessor__transformer_weights | |
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+ | preprocessor__transformers | [('num', Pipeline(steps=[('imputer', SimpleImputer(strategy='median')),
143
+ ('std_scaler', StandardScaler())]), ['MonthlyCharges', 'TotalCharges', 'tenure']), ('cat', OneHotEncoder(), ['SeniorCitizen', 'gender', 'Partner', 'Dependents', 'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling', 'PaymentMethod'])] |
144
+ | preprocessor__verbose | False |
145
+ | preprocessor__verbose_feature_names_out | True |
146
+ | preprocessor__num | Pipeline(steps=[('imputer', SimpleImputer(strategy='median')),
147
+ ('std_scaler', StandardScaler())]) |
148
+ | preprocessor__cat | OneHotEncoder() |
149
+ | preprocessor__num__memory | |
150
+ | preprocessor__num__steps | [('imputer', SimpleImputer(strategy='median')), ('std_scaler', StandardScaler())] |
151
+ | preprocessor__num__verbose | False |
152
+ | preprocessor__num__imputer | SimpleImputer(strategy='median') |
153
+ | preprocessor__num__std_scaler | StandardScaler() |
154
+ | preprocessor__num__imputer__add_indicator | False |
155
+ | preprocessor__num__imputer__copy | True |
156
+ | preprocessor__num__imputer__fill_value | |
157
+ | preprocessor__num__imputer__missing_values | nan |
158
+ | preprocessor__num__imputer__strategy | median |
159
+ | preprocessor__num__imputer__verbose | deprecated |
160
+ | preprocessor__num__std_scaler__copy | True |
161
+ | preprocessor__num__std_scaler__with_mean | True |
162
+ | preprocessor__num__std_scaler__with_std | True |
163
+ | preprocessor__cat__categories | auto |
164
+ | preprocessor__cat__drop | |
165
+ | preprocessor__cat__dtype | <class 'numpy.float64'> |
166
+ | preprocessor__cat__handle_unknown | error |
167
+ | preprocessor__cat__max_categories | |
168
+ | preprocessor__cat__min_frequency | |
169
+ | preprocessor__cat__sparse | True |
170
+ | classifier__C | 1.0 |
171
+ | classifier__class_weight | balanced |
172
+ | classifier__dual | False |
173
+ | classifier__fit_intercept | True |
174
+ | classifier__intercept_scaling | 1 |
175
+ | classifier__l1_ratio | |
176
+ | classifier__max_iter | 300 |
177
+ | classifier__multi_class | auto |
178
+ | classifier__n_jobs | |
179
+ | classifier__penalty | l2 |
180
+ | classifier__random_state | |
181
+ | classifier__solver | lbfgs |
182
+ | classifier__tol | 0.0001 |
183
+ | classifier__verbose | 0 |
184
+ | classifier__warm_start | False |
185
+
186
+ </details>
187
+
188
+ ### Model Plot
189
+
190
+ The model plot is below.
191
+
192
+ <style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-3" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,ColumnTransformer(transformers=[(&#x27;num&#x27;,Pipeline(steps=[(&#x27;imputer&#x27;,SimpleImputer(strategy=&#x27;median&#x27;)),(&#x27;std_scaler&#x27;,StandardScaler())]),[&#x27;MonthlyCharges&#x27;,&#x27;TotalCharges&#x27;, &#x27;tenure&#x27;]),(&#x27;cat&#x27;, OneHotEncoder(),[&#x27;SeniorCitizen&#x27;, &#x27;gender&#x27;,&#x27;Partner&#x27;, &#x27;Dependents&#x27;,&#x27;PhoneService&#x27;,&#x27;MultipleLines&#x27;,&#x27;InternetService&#x27;,&#x27;OnlineSecurity&#x27;,&#x27;OnlineBackup&#x27;,&#x27;DeviceProtection&#x27;,&#x27;TechSupport&#x27;, &#x27;StreamingTV&#x27;,&#x27;StreamingMovies&#x27;,&#x27;Contract&#x27;,&#x27;PaperlessBilling&#x27;,&#x27;PaymentMethod&#x27;])])),(&#x27;classifier&#x27;,LogisticRegression(class_weight=&#x27;balanced&#x27;, max_iter=300))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-10" type="checkbox" ><label for="sk-estimator-id-10" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,ColumnTransformer(transformers=[(&#x27;num&#x27;,Pipeline(steps=[(&#x27;imputer&#x27;,SimpleImputer(strategy=&#x27;median&#x27;)),(&#x27;std_scaler&#x27;,StandardScaler())]),[&#x27;MonthlyCharges&#x27;,&#x27;TotalCharges&#x27;, &#x27;tenure&#x27;]),(&#x27;cat&#x27;, OneHotEncoder(),[&#x27;SeniorCitizen&#x27;, &#x27;gender&#x27;,&#x27;Partner&#x27;, &#x27;Dependents&#x27;,&#x27;PhoneService&#x27;,&#x27;MultipleLines&#x27;,&#x27;InternetService&#x27;,&#x27;OnlineSecurity&#x27;,&#x27;OnlineBackup&#x27;,&#x27;DeviceProtection&#x27;,&#x27;TechSupport&#x27;, &#x27;StreamingTV&#x27;,&#x27;StreamingMovies&#x27;,&#x27;Contract&#x27;,&#x27;PaperlessBilling&#x27;,&#x27;PaymentMethod&#x27;])])),(&#x27;classifier&#x27;,LogisticRegression(class_weight=&#x27;balanced&#x27;, max_iter=300))])</pre></div></div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-11" type="checkbox" ><label for="sk-estimator-id-11" class="sk-toggleable__label sk-toggleable__label-arrow">preprocessor: ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,Pipeline(steps=[(&#x27;imputer&#x27;,SimpleImputer(strategy=&#x27;median&#x27;)),(&#x27;std_scaler&#x27;,StandardScaler())]),[&#x27;MonthlyCharges&#x27;, &#x27;TotalCharges&#x27;, &#x27;tenure&#x27;]),(&#x27;cat&#x27;, OneHotEncoder(),[&#x27;SeniorCitizen&#x27;, &#x27;gender&#x27;, &#x27;Partner&#x27;,&#x27;Dependents&#x27;, &#x27;PhoneService&#x27;, &#x27;MultipleLines&#x27;,&#x27;InternetService&#x27;, &#x27;OnlineSecurity&#x27;,&#x27;OnlineBackup&#x27;, &#x27;DeviceProtection&#x27;,&#x27;TechSupport&#x27;, &#x27;StreamingTV&#x27;,&#x27;StreamingMovies&#x27;, &#x27;Contract&#x27;,&#x27;PaperlessBilling&#x27;, &#x27;PaymentMethod&#x27;])])</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-12" type="checkbox" ><label for="sk-estimator-id-12" class="sk-toggleable__label sk-toggleable__label-arrow">num</label><div class="sk-toggleable__content"><pre>[&#x27;MonthlyCharges&#x27;, &#x27;TotalCharges&#x27;, &#x27;tenure&#x27;]</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-13" type="checkbox" ><label for="sk-estimator-id-13" class="sk-toggleable__label sk-toggleable__label-arrow">SimpleImputer</label><div class="sk-toggleable__content"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-14" type="checkbox" ><label for="sk-estimator-id-14" class="sk-toggleable__label sk-toggleable__label-arrow">StandardScaler</label><div class="sk-toggleable__content"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-15" type="checkbox" ><label for="sk-estimator-id-15" class="sk-toggleable__label sk-toggleable__label-arrow">cat</label><div class="sk-toggleable__content"><pre>[&#x27;SeniorCitizen&#x27;, &#x27;gender&#x27;, &#x27;Partner&#x27;, &#x27;Dependents&#x27;, &#x27;PhoneService&#x27;, &#x27;MultipleLines&#x27;, &#x27;InternetService&#x27;, &#x27;OnlineSecurity&#x27;, &#x27;OnlineBackup&#x27;, &#x27;DeviceProtection&#x27;, &#x27;TechSupport&#x27;, &#x27;StreamingTV&#x27;, &#x27;StreamingMovies&#x27;, &#x27;Contract&#x27;, &#x27;PaperlessBilling&#x27;, &#x27;PaymentMethod&#x27;]</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-16" type="checkbox" ><label for="sk-estimator-id-16" class="sk-toggleable__label sk-toggleable__label-arrow">OneHotEncoder</label><div class="sk-toggleable__content"><pre>OneHotEncoder()</pre></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-17" type="checkbox" ><label for="sk-estimator-id-17" class="sk-toggleable__label sk-toggleable__label-arrow">LogisticRegression</label><div class="sk-toggleable__content"><pre>LogisticRegression(class_weight=&#x27;balanced&#x27;, max_iter=300)</pre></div></div></div></div></div></div></div>
193
+
194
+ ## Evaluation Results
195
+
196
+ You can find the details about evaluation process and the evaluation results.
197
+
198
+
199
+
200
+ | Metric | Value |
201
+ |----------|----------|
202
+ | accuracy | 0.730305 |
203
+ | f1 score | 0.730305 |
204
+
205
+ # How to Get Started with the Model
206
+
207
+ Use the code below to get started with the model.
208
+
209
+ <details>
210
+ <summary> Click to expand </summary>
211
+
212
+ ```python
213
+ import pickle
214
+ with open(dtc_pkl_filename, 'rb') as file:
215
+ clf = pickle.load(file)
216
+ ```
217
+
218
+ </details>
219
+
220
+
221
+
222
+
223
+ # Model Card Authors
224
+
225
+ This model card is written by following authors:
226
+
227
+ skops_user
228
+
229
+ # Model Card Contact
230
+
231
+ You can contact the model card authors through following channels:
232
+ [More Information Needed]
233
+
234
+ # Citation
235
+
236
+ Below you can find information related to citation.
237
+
238
+ **BibTeX:**
239
+ ```
240
+ bibtex
241
+ @inproceedings{...,year={2020}}
242
+ ```
243
+
244
+
245
+ # Additional Content
246
+
247
+ ## confusion_matrix
248
+
249
+ ![confusion_matrix](confusion_matrix.png)
config.json ADDED
@@ -0,0 +1,129 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "sklearn": {
3
+ "columns": [
4
+ "gender",
5
+ "SeniorCitizen",
6
+ "Partner",
7
+ "Dependents",
8
+ "tenure",
9
+ "PhoneService",
10
+ "MultipleLines",
11
+ "InternetService",
12
+ "OnlineSecurity",
13
+ "OnlineBackup",
14
+ "DeviceProtection",
15
+ "TechSupport",
16
+ "StreamingTV",
17
+ "StreamingMovies",
18
+ "Contract",
19
+ "PaperlessBilling",
20
+ "PaymentMethod",
21
+ "MonthlyCharges",
22
+ "TotalCharges"
23
+ ],
24
+ "environment": [
25
+ "scikit-learn=1.1.1"
26
+ ],
27
+ "example_input": {
28
+ "Contract": [
29
+ "Two year",
30
+ "Month-to-month",
31
+ "One year"
32
+ ],
33
+ "Dependents": [
34
+ "Yes",
35
+ "No",
36
+ "No"
37
+ ],
38
+ "DeviceProtection": [
39
+ "No",
40
+ "No",
41
+ "Yes"
42
+ ],
43
+ "InternetService": [
44
+ "Fiber optic",
45
+ "Fiber optic",
46
+ "DSL"
47
+ ],
48
+ "MonthlyCharges": [
49
+ 79.05,
50
+ 84.95,
51
+ 68.8
52
+ ],
53
+ "MultipleLines": [
54
+ "Yes",
55
+ "Yes",
56
+ "Yes"
57
+ ],
58
+ "OnlineBackup": [
59
+ "No",
60
+ "No",
61
+ "Yes"
62
+ ],
63
+ "OnlineSecurity": [
64
+ "Yes",
65
+ "No",
66
+ "Yes"
67
+ ],
68
+ "PaperlessBilling": [
69
+ "No",
70
+ "Yes",
71
+ "No"
72
+ ],
73
+ "Partner": [
74
+ "Yes",
75
+ "Yes",
76
+ "No"
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+ ],
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+ "PaymentMethod": [
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+ "Bank transfer (automatic)",
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+ "Electronic check",
81
+ "Bank transfer (automatic)"
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+ ],
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+ "PhoneService": [
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+ "Yes",
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+ "Yes",
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+ "Yes"
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+ "SeniorCitizen": [
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+ ],
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+ "StreamingMovies": [
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+ "No",
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+ "No",
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+ "No"
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+ ],
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+ "StreamingTV": [
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+ "No",
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+ "Yes",
101
+ "No"
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+ ],
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+ "TechSupport": [
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+ "No",
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+ "No",
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+ "Yes"
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+ ],
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+ "TotalCharges": [
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+ 5730.7,
110
+ 1378.25,
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+ 4111.35
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+ ],
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+ "gender": [
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+ "Female",
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+ "Female",
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+ "Male"
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+ ],
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+ "tenure": [
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+ 63
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+ ]
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+ },
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+ "model": {
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+ "file": "example.pkl"
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+ },
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+ "task": "tabular-classification"
128
+ }
129
+ }
confusion_matrix.png ADDED
example.pkl ADDED
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