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ricklamersricklamers
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Update environment
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-43
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8 files changed

+407
-43
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‎.orchest/environments/c56ab762-539c-4cce-9b1e-c4b00300ec6f/properties.json

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{"uuid": "c56ab762-539c-4cce-9b1e-c4b00300ec6f", "gpu_support": false, "base_image": "orchest/base-kernel-py", "name": "Python 3", "language": "python"}
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{"gpu_support": false, "base_image": "orchest/base-kernel-py", "name": "Python 3", "language": "python", "uuid": "c56ab762-539c-4cce-9b1e-c4b00300ec6f"}

‎.orchest/environments/c56ab762-539c-4cce-9b1e-c4b00300ec6f/setup_script.sh

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# Install additional packages used throughout the pipeline. If
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# you want more granularity, you can use different environments
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# for each step.
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pip install vaex
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pip install --user vaex numpy==1.20

‎collect-results.ipynb

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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"linear-regression-rmse 0.7297641969632994\n",
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"\n",
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"decision-tree-regressor-rmse 0.7277118487138823\n",
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"\n",
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"svm-regressor-rmse 0.680488796037178\n"
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]
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}
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],
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"source": [
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"for name, value in data.items():\n",
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" if name != \"unnamed\":\n",

‎decision-tree.ipynb

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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Fitting the model...\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=None,\n",
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" max_features=None, max_leaf_nodes=None,\n",
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" min_impurity_decrease=0.0, min_impurity_split=None,\n",
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" min_samples_leaf=1, min_samples_split=2,\n",
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" min_weight_fraction_leaf=0.0, presort='deprecated',\n",
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" random_state=None, splitter='best')"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"print(\"Fitting the model...\")\n",
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"\n",
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [

‎explore-data.ipynb

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‎linear-regression.ipynb

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},
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},
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Fitting the model...\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"print(\"Fitting the model...\")\n",
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"\n",
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},
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"cell_type": "code",
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},
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"execution_count": 5,
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"metadata": {},
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‎pre-processing.ipynb

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},
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"execution_count": 2,
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"metadata": {},
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Scaling the data...\n"
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]
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}
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],
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"source": [
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"# Print messages are useful when you are keeping an eye on the logs of\n",
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"# a pipeline step.\n",
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},
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‎svm.ipynb

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},
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},
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},
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"metadata": {},
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"outputs": [
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"output_type": "stream",
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"text": [
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"Fitting the model...\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='scale',\n",
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" kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"print(\"Fitting the model...\")\n",
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},
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},
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"execution_count": 5,
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"metadata": {},
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"source": [

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