diff --git a/.gitignore b/.gitignore index cd56dce1..186220be 100644 --- a/.gitignore +++ b/.gitignore @@ -5,6 +5,11 @@ ## OS configs .DS_Store +# Project +data/* +models/* +reports/* + # Python __pycache__ .ipynb_checkpoints diff --git a/README.md b/README.md index 83039e3a..b7bd11e6 100644 --- a/README.md +++ b/README.md @@ -5,7 +5,7 @@ ### 1. Fork / Clone this repository ```bash -git clone https://gitlab.com/iterative.ai/cse/tutorials/course-ds-base.git +git clone https://github.com/iterative/course-ds-base.git cd course-ds-base ``` @@ -15,6 +15,7 @@ cd course-ds-base Create virtual environment named `dvc-venv` (you may use other name) ```bash python3 -m venv dvc-venv +echo "export PYTHONPATH=$PWD" >> dvc-venv/bin/activate source dvc-venv/bin/activate ``` Install python libraries @@ -30,9 +31,13 @@ Add Virtual Environment to Jupyter Notebook python -m ipykernel install --user --name=dvc-venv ``` -Configure ToC for jupyter notebook (optional) +Configure ToC for jupyter notebook (optional)/Install the python package ```bash + + +pip install jupyter_contrib_nbextensions + jupyter contrib nbextension install --user jupyter nbextension enable toc2/main ``` diff --git a/file.txt b/file.txt new file mode 100644 index 00000000..e69de29b diff --git a/lineapy-trial-prototype.ipynb b/lineapy-trial-prototype.ipynb new file mode 100644 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"execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: pandas==1.3.2 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (1.3.2)\n", + "Requirement already satisfied: python-dateutil>=2.7.3 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from pandas==1.3.2) (2.8.1)\n", + "Requirement already satisfied: numpy>=1.17.3 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from pandas==1.3.2) (1.18.5)\n", + "Requirement already satisfied: pytz>=2017.3 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from pandas==1.3.2) (2022.1)\n", + "Requirement already satisfied: six>=1.5 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from python-dateutil>=2.7.3->pandas==1.3.2) (1.15.0)\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: 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mlflow_registry_uri=None, mlflow_tracking_uri=None, default_ml_models_storage_backend=None)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lineapy.options" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-16T21:17:31.460557Z", + "start_time": "2019-06-16T21:17:29.395297Z" + } + }, + "outputs": [], + "source": [ + "import lineapy\n", + "import joblib\n", + "import json\n", + "import itertools\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn.metrics import confusion_matrix, f1_score\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.model_selection import train_test_split\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-16T21:17:31.485189Z", + "start_time": "2019-06-16T21:17:31.473720Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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sepal length (cm)sepal width (cm)petal length (cm)petal width (cm)target
05.13.51.40.20
14.93.01.40.20
24.73.21.30.20
34.63.11.50.20
45.03.61.40.20
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" + ], + "text/plain": [ + " sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) \\\n", + "0 5.1 3.5 1.4 0.2 \n", + "1 4.9 3.0 1.4 0.2 \n", + "2 4.7 3.2 1.3 0.2 \n", + "3 4.6 3.1 1.5 0.2 \n", + "4 5.0 3.6 1.4 0.2 \n", + "\n", + " target \n", + "0 0 \n", + "1 0 \n", + "2 0 \n", + "3 0 \n", + "4 0 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Get data \n", + "\n", + "import pandas as pd\n", + "from sklearn.datasets import load_iris\n", + "\n", + "data = load_iris(as_frame=True)\n", + "dataset = data.frame\n", + "dataset.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0: setosa\n", + "1: versicolor\n", + "2: virginica\n" + ] + }, + { + "data": { + "text/plain": [ + "[None, None, None]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# print labels for target values \n", + "\n", + "[print(f'{target}: {label}') for target, label in zip(data.target.unique(), data.target_names)]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-16T21:17:32.328046Z", + "start_time": "2019-06-16T21:17:32.323611Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['sepal_length', 'sepal_width', 'petal_length', 'petal_width']" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# feature names\n", + "\n", + "dataset.columns = [colname.strip(' (cm)').replace(' ', '_') for colname in dataset.columns.tolist()]\n", + "\n", + "feature_names = dataset.columns.tolist()[:4]\n", + "feature_names" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "#save raw data as artifact\n", + "dataset_csv = './data/raw/iris.csv'\n", + "dataset.to_csv(dataset_csv, index=False)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width target\n", + "0 5.1 3.5 1.4 0.2 0\n", + "1 4.9 3.0 1.4 0.2 0\n", + "2 4.7 3.2 1.3 0.2 0\n", + "3 4.6 3.1 1.5 0.2 0\n", + "4 5.0 3.6 1.4 0.2 0\n", + ".. ... ... ... ... ...\n", + "145 6.7 3.0 5.2 2.3 2\n", + "146 6.3 2.5 5.0 1.9 2\n", + "147 6.5 3.0 5.2 2.0 2\n", + "148 6.2 3.4 5.4 2.3 2\n", + "149 5.9 3.0 5.1 1.8 2\n", + "\n", + "[150 rows x 5 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.3.2\n" + ] + } + ], + "source": [ + "print(pd.__version__)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LineaArtifact(name='iris-raw', _version=4)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#save raw data as artifact to lineapy\n", + "lineapy.save(dataset, \"iris-raw\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Features engineering" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-16T21:21:02.150708Z", + "start_time": "2019-06-16T21:21:02.144518Z" + } + }, + "outputs": [], + "source": [ + "dataset['sepal_length_to_sepal_width'] = dataset['sepal_length'] / dataset['sepal_width']\n", + "dataset['petal_length_to_petal_width'] = dataset['petal_length'] / dataset['petal_width']\n", + "\n", + "dataset = dataset[[\n", + " 'sepal_length', 'sepal_width', 'petal_length', 'petal_width',\n", + "# 'sepal_length_in_square', 'sepal_width_in_square', 'petal_length_in_square', 'petal_width_in_square',\n", + " 'sepal_length_to_sepal_width', 'petal_length_to_petal_width',\n", + " 'target'\n", + "]]" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-16T21:21:02.987144Z", + "start_time": "2019-06-16T21:21:02.976092Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width \\\n", + "0 5.1 3.5 1.4 0.2 \n", + "1 4.9 3.0 1.4 0.2 \n", + "2 4.7 3.2 1.3 0.2 \n", + "3 4.6 3.1 1.5 0.2 \n", + "4 5.0 3.6 1.4 0.2 \n", + "\n", + " sepal_length_to_sepal_width petal_length_to_petal_width target \n", + "0 1.457143 7.0 0 \n", + "1 1.633333 7.0 0 \n", + "2 1.468750 6.5 0 \n", + "3 1.483871 7.5 0 \n", + "4 1.388889 7.0 0 " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# Save features\n", + "features_path = './data/processed/featured_iris.csv'\n", + "dataset.to_csv(features_path, index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LineaArtifact(name='iris-preprocessed', _version=4)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#save features to lineapy\n", + "lineapy.save(dataset, \"iris-preprocessed\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Split dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-16T21:21:06.361378Z", + "start_time": "2019-06-16T21:21:06.358647Z" + } + }, + "outputs": [], + "source": [ + "test_size=0.2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Splittail train/test" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-16T21:21:07.438133Z", + "start_time": "2019-06-16T21:21:07.431649Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "((120, 7), (30, 7))" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_dataset, test_dataset = train_test_split(dataset, test_size=test_size, random_state=42)\n", + "train_dataset.shape, test_dataset.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# Save train and test sets\n", + "trainset_path = './data/processed/train_iris.csv'\n", + "testset_path = './data/processed/test_iris.csv'\n", + "\n", + "train_dataset.to_csv(trainset_path)\n", + "test_dataset.to_csv(testset_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LineaArtifact(name='test-dataset', _version=4)" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#save train and test sets to lineapy\n", + "lineapy.save(train_dataset, \"train-dataset\")\n", + "lineapy.save(test_dataset, \"test-dataset\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Train" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-16T21:21:10.932148Z", + "start_time": "2019-06-16T21:21:10.927844Z" + } + }, + "outputs": [], + "source": [ + "# Get X and Y\n", + "\n", + "y_train = train_dataset.loc[:, 'target'].values.astype('int32')\n", + "X_train = train_dataset.drop('target', axis=1).values.astype('float32')" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-16T21:21:55.427365Z", + "start_time": "2019-06-16T21:21:55.416431Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "LogisticRegression(C=0.001, multi_class='multinomial')" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create an instance of Logistic Regression Classifier CV and fit the data\n", + "\n", + "logreg = LogisticRegression(C=0.001, solver='lbfgs', multi_class='multinomial', max_iter=100)\n", + "logreg.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['./models/model.joblib']" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model_path= './models/model.joblib'\n", + "joblib.dump(logreg, model_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LineaArtifact(name='logreg-model', _version=3)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#save model to lineapy\n", + "lineapy.save(model_path, \"logreg-model\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Evaluate" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-16T21:21:55.875303Z", + "start_time": "2019-06-16T21:21:55.864724Z" + } + }, + "outputs": [], + "source": [ + "def plot_confusion_matrix(cm,\n", + " target_names,\n", + " title='Confusion matrix',\n", + " cmap=None,\n", + " normalize=True):\n", + " \"\"\"\n", + " given a sklearn confusion matrix (cm), make a nice plot\n", + "\n", + " Arguments\n", + " ---------\n", + " cm: confusion matrix from sklearn.metrics.confusion_matrix\n", + "\n", + " target_names: given classification classes such as [0, 1, 2]\n", + " the class names, for example: ['high', 'medium', 'low']\n", + "\n", + " title: the text to display at the top of the matrix\n", + "\n", + " cmap: the gradient of the values displayed from matplotlib.pyplot.cm\n", + " see http://matplotlib.org/examples/color/colormaps_reference.html\n", + " plt.get_cmap('jet') or plt.cm.Blues\n", + "\n", + " normalize: If False, plot the raw numbers\n", + " If True, plot the proportions\n", + "\n", + " Usage\n", + " -----\n", + " plot_confusion_matrix(cm = cm, # confusion matrix created by\n", + " # sklearn.metrics.confusion_matrix\n", + " normalize = True, # show proportions\n", + " target_names = y_labels_vals, # list of names of the classes\n", + " title = best_estimator_name) # title of graph\n", + "\n", + " Citiation\n", + " ---------\n", + " http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html\n", + "\n", + " \"\"\"\n", + "\n", + " accuracy = np.trace(cm) / float(np.sum(cm))\n", + " misclass = 1 - accuracy\n", + "\n", + " if cmap is None:\n", + " cmap = plt.get_cmap('Blues')\n", + "\n", + " plt.figure(figsize=(8, 6))\n", + " plt.imshow(cm, interpolation='nearest', cmap=cmap)\n", + " plt.title(title)\n", + " plt.colorbar()\n", + "\n", + " if target_names is not None:\n", + " tick_marks = np.arange(len(target_names))\n", + " plt.xticks(tick_marks, target_names, rotation=45)\n", + " plt.yticks(tick_marks, target_names)\n", + "\n", + " if normalize:\n", + " cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n", + "\n", + " thresh = cm.max() / 1.5 if normalize else cm.max() / 2\n", + " for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n", + " if normalize:\n", + " plt.text(j, i, \"{:0.4f}\".format(cm[i, j]),\n", + " horizontalalignment=\"center\",\n", + " color=\"white\" if cm[i, j] > thresh else \"black\")\n", + " else:\n", + " plt.text(j, i, \"{:,}\".format(cm[i, j]),\n", + " horizontalalignment=\"center\",\n", + " color=\"white\" if cm[i, j] > thresh else \"black\")\n", + "\n", + " plt.tight_layout()\n", + " plt.ylabel('True label')\n", + " plt.xlabel('Predicted label\\naccuracy={:0.4f}; misclass={:0.4f}'.format(accuracy, misclass))\n", + " \n", + " return plt.gcf()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-16T21:21:56.090756Z", + "start_time": "2019-06-16T21:21:56.086966Z" + } + }, + "outputs": [], + "source": [ + "# Get X and Y\n", + "\n", + "y_test = test_dataset.loc[:, 'target'].values.astype('int32')\n", + "X_test = test_dataset.drop('target', axis=1).values.astype('float32')" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-16T21:21:56.270245Z", + "start_time": "2019-06-16T21:21:56.265054Z" + } + }, + "outputs": [], + "source": [ + "prediction = logreg.predict(X_test)\n", + "cm = confusion_matrix(prediction, y_test)\n", + "f1 = f1_score(y_true = y_test, y_pred = prediction, average='macro')" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-16T21:21:56.493617Z", + "start_time": "2019-06-16T21:21:56.489929Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9305555555555555" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# f1 score value\n", + "f1" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "# Save metrics\n", + "metrics_file = './reports/metrics.json'\n", + "\n", + "metrics = {\n", + " 'f1': f1\n", + "}\n", + "\n", + "with open(metrics_file, 'w') as mf:\n", + " json.dump(\n", + " obj=metrics,\n", + " fp=mf,\n", + " indent=4\n", + " )\n" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "cm_plot = plot_confusion_matrix(cm, data.target_names, normalize=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "# Save confusion matrix image\n", + "confusion_matrix_image = './reports/confusion_matrix.png'\n", + "cm_plot.savefig(confusion_matrix_image)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LineaArtifact(name='plot-confusion-matrix', _version=1)" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#save confusion matrix to lineapy\n", + "lineapy.save(plot_confusion_matrix, \"plot-confusion-matrix\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#commenting for change\n" + ] + } + ], + "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": "3.8.5" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": true + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/requirements.txt b/requirements.txt index d5b4910e..95cfe9b2 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,13 +1,13 @@ -dvc==2.6.4 -joblib==1.0.1 -jupyter==1.0.0 -jupyter_contrib_nbextensions==0.5.1 -matplotlib==3.4.3 -numpy==1.21.2 -pandas==1.3.2 -pytest==6.2.4 -python-box==5.4.1 -pyyaml==5.4.1 -scikit-learn==0.24.2 -scipy==1.7.1 -tqdm==4.62.2 \ No newline at end of file +dvc>=2.8.3,<3 +joblib>=1.0.1,<2 +jupyter>=1.0.0,<2 +jupyter_contrib_nbextensions>=0.5.1,<1 +matplotlib>=3.4.3,<4 +numpy>=1.21.2,<2 +pandas>=1.3.2,<2 +pytest>=6.2.4,<7 +python-box>=5.4.1,<6 +pyyaml>=5.4.1,<6 +scikit-learn>=0.24.2,<2 +scipy>=1.7.1,<2 +tqdm>=4.62.2,<5 diff --git a/step-0-prototype.ipynb b/step-0-prototype.ipynb index 3f7fee6c..608a10f2 100644 --- a/step-0-prototype.ipynb +++ b/step-0-prototype.ipynb @@ -383,7 +383,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.9.2" }, "toc": { "base_numbering": 1,