From 6e2cbe667333bad0873d8957669ef8eda96d5c97 Mon Sep 17 00:00:00 2001 From: DawidPludowski <72541839+DawidPludowski@users.noreply.github.com> Date: Thu, 31 Mar 2022 14:32:56 +0200 Subject: [PATCH 1/5] Add hw1 Pludowski Dawid --- .../Homework-I/Pludowski/PludowskiD.html | 15007 ++++++++++++++++ .../Homework-I/Pludowski/PludowskiD.ipynb | 2020 +++ 2 files changed, 17027 insertions(+) create mode 100644 Homeworks/Homework-I/Pludowski/PludowskiD.html create mode 100644 Homeworks/Homework-I/Pludowski/PludowskiD.ipynb diff --git a/Homeworks/Homework-I/Pludowski/PludowskiD.html b/Homeworks/Homework-I/Pludowski/PludowskiD.html new file mode 100644 index 0000000..093a91a --- /dev/null +++ b/Homeworks/Homework-I/Pludowski/PludowskiD.html @@ -0,0 +1,15007 @@ + + + + + +hw1 + + + + + + + + + + + + + + + + + + + + + + + + +
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HW 1 - Dawid Pludowski

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Workspace preparation

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importing libraries

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Loading model and test data

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Please note that train data is not provided here as model was trained in different noebook to remove redundant code.

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Observation explaining

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Creating observation object

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Choosing two observations

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Assuring that model prediction are close to real target value

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Model predict target very well, so further explanation can be present.

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Creating breakdown and shapley plots for choosen observations

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Based on break_down plot, the greatest positive inpact on prediction has total_rooms and longitude. However, longitude without latitude does not create significant information and we may expect that only interaction of that variables really matters in the model.

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The greates negative impact has ocean_proximity and latitude. Again, only in interaction latitude create siginifact infromation.

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On shap plot, households's impact is positive while on break_down it is negative. It may suggest that interaction between households and other variables exists. We may expect interaction with total_* or population variables, as ratio of that variables tell more about housing in the area than single variables.

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While in first observation most variables has negative impact, in the second observation they have mainly positive impact. It is worth mentioning that the greatest negative impact in second observation is observed in households and total_rooms while they have the greatest positive impact in the first observation. The linear positive correlation between target and those two variables should be examinated.

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Conclusion

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Since values obtained by break_down and shap are different, some interactions in model exist. We can easily find them using break_down_interactions plots:

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For each observation the interactions are different; however, we can notice that:

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+ + + + + + + + + diff --git a/Homeworks/Homework-I/Pludowski/PludowskiD.ipynb b/Homeworks/Homework-I/Pludowski/PludowskiD.ipynb new file mode 100644 index 0000000..4fb2440 --- /dev/null +++ b/Homeworks/Homework-I/Pludowski/PludowskiD.ipynb @@ -0,0 +1,2020 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# HW 1 - Dawid Pludowski" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Workspace preparation\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### importing libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "\n", + "import pickle\n", + "\n", + "import dalex as dx\n", + "\n", + "# libraries that are used in creating objects from pickle\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "\n", + "from sklearn.compose import ColumnTransformer\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "from sklearn.impute import SimpleImputer\n", + "from sklearn.preprocessing import OrdinalEncoder\n", + "from sklearn.preprocessing import OneHotEncoder\n", + "\n", + "from sklearn.base import BaseEstimator, TransformerMixin" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loading model and test data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# code necessery to load model from pickle\n", + "\n", + "rooms_ix, bedrooms_ix, population_ix, households_ix = 3, 4, 5, 6\n", + "\n", + "class CombinedAttributesAdder(BaseEstimator, TransformerMixin):\n", + " def __init__(self, add_bedrooms_per_room = True): \n", + " self.add_bedrooms_per_room = add_bedrooms_per_room\n", + " def fit(self, X, y=None):\n", + " return self\n", + " def transform(self, X):\n", + " rooms_per_household = X[:, rooms_ix] / X[:, households_ix]\n", + " population_per_household = X[:, population_ix] / X[:, households_ix]\n", + " if self.add_bedrooms_per_room:\n", + " bedrooms_per_room = X[:, bedrooms_ix] / X[:, rooms_ix]\n", + " return np.c_[X, rooms_per_household, population_per_household,\n", + " bedrooms_per_room]\n", + " else:\n", + " return np.c_[X, rooms_per_household, population_per_household]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "with open('full_model.pkl', 'rb') as f:\n", + " model = pickle.load(f)\n", + "with open('test_dataset.pkl', 'rb') as f:\n", + " test_data = pickle.load(f)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "X = test_data.drop(columns=['median_house_value'])\n", + "y = test_data['median_house_value']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Please note that train data is not provided here as model was trained in different noebook to remove redundant code." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Observation explaining" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating observation object" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Preparation of a new explainer is initiated\n", + "\n", + " -> data : 4128 rows 10 cols\n", + " -> target variable : Parameter 'y' was a pandas.Series. Converted to a numpy.ndarray.\n", + " -> target variable : 4128 values\n", + " -> model_class : sklearn.ensemble._forest.RandomForestRegressor (default)\n", + " -> label : housing RF Pipeline\n", + " -> predict function : will be used (default)\n", + " -> predict function : Accepts only pandas.DataFrame, numpy.ndarray causes problems.\n", + " -> predicted values : min = 4.95e+04, mean = 2.08e+05, max = 5e+05\n", + " -> model type : regression will be used (default)\n", + " -> residual function : difference between y and yhat (default)\n", + " -> residuals : min = -2.49e+05, mean = -1.66e+03, max = 2.94e+05\n", + " -> model_info : package sklearn\n", + "\n", + "A new explainer has been created!\n" + ] + } + ], + "source": [ + "model_exp = dx.Explainer(model, X, y, \n", + " label = \"housing RF Pipeline\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Choosing two observations" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "observation_1 = X.iloc[[5]]\n", + "observation_2 = X.iloc[[321]]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Assuring that model prediction are close to real target value" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Real value of observation 1: 120600; predicted value: 137187\n", + "Real value of observation 2: 298900; predicted value: 275727\n" + ] + } + ], + "source": [ + "prediction_1 = model.predict(observation_1)\n", + "prediction_2 = model.predict(observation_2)\n", + "\n", + "print(\"Real value of observation 1: {y_1:.0f}; predicted value: {y_1_hat:.0f}\".format(y_1=list(y.iloc[[5]])[0], y_1_hat=prediction_1[0]))\n", + "print(\"Real value of observation 2: {y_2:.0f}; predicted value: {y_2_hat:.0f}\".format(y_2=list(y.iloc[[321]])[0], y_2_hat=prediction_2[0]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Model predict `target` very well, so further explanation can be present." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating breakdown and shapley plots for choosen observations" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "order = X.columns.to_list()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "displaylogo": false, + "modeBarButtonsToRemove": [ + "sendDataToCloud", + "lasso2d", + "autoScale2d", + "select2d", + "zoom2d", + "pan2d", + "zoomIn2d", + "zoomOut2d", + "resetScale2d", + "toggleSpikelines", + "hoverCompareCartesian", + "hoverClosestCartesian" + ], + "plotlyServerURL": "https://plot.ly", + "staticPlot": false, + "toImageButtonOptions": { + "height": null, + "width": null + } + }, + "data": [ + { + "base": 207980.2223998708, + "connector": { + "line": { + "color": "#371ea3", + "dash": "solid", + "width": 1 + }, + "mode": "spanning" + }, + "decreasing": { + "marker": { + "color": "#f05a71" + } + }, + "hoverinfo": "text+delta", + "hoverlabel": { + "bgcolor": "rgba(0,0,0,0.8)" + }, + "hovertext": [ + "Average response: 207980.222
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decreases average response by", + "total_rooms = 3851.0
increases average response by", + "total_bedrooms = 892.0
decreases average response by", + "population = 1847.0
decreases average response by", + "households = 747.0
decreases average response by", + "median_income = 3.433
decreases average response by", + "ocean_proximity = INLAND
decreases average response by", + "income_cat = 3.0
decreases average response by", + "Average response: 207980.222
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Prediction: 137186.667
ocean_proximity = INLAND
decreases average response
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Prediction: 137186.667
latitude = 38.68
decreases average response
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Prediction: 137186.667
households = 747.0
increases average response
by 17713.758", + "Average response: 207980.222
Prediction: 137186.667
longitude = -121.8
increases average response
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Prediction: 137186.667
population = 1847.0
decreases average response
by 12473.348", + "Average response: 207980.222
Prediction: 137186.667
median_income = 3.433
decreases average response
by 10460.156", + "Average response: 207980.222
Prediction: 137186.667
total_bedrooms = 892.0
decreases average response
by 8836.081", + "Average response: 207980.222
Prediction: 137186.667
total_rooms = 3851.0
increases average response
by 8486.319", + "Average response: 207980.222
Prediction: 137186.667
income_cat = 3.0
decreases average response
by 6881.122", + "Average response: 207980.222
Prediction: 137186.667
housing_median_age = 11.0
decreases average response
by 5376.908" + ], + "marker": { + "color": [ + "#f05a71", + "#f05a71", + "#8bdcbe", + "#8bdcbe", + "#f05a71", + "#f05a71", + "#f05a71", + "#8bdcbe", + "#f05a71", + "#f05a71" + ] + }, + "orientation": "h", + "showlegend": false, + "text": [ + "-37312.288", + "-28722.273", + "+17713.758", + "+13068.543", + "-12473.348", + "-10460.156", + "-8836.081", + "+8486.319", + "-6881.122", + "-5376.908" + ], + "textposition": "outside", + "type": "bar", + "x": [ + -37312.288, + -28722.273, + 17713.758, + 13068.543, + -12473.348, + -10460.156, + -8836.081, + 8486.319, + -6881.122, + -5376.908 + ], + "xaxis": "x", + "y": [ + "ocean_proximity = INLAND", + "latitude = 38.68", + "households = 747.0", + "longitude = -121.8", + "population = 1847.0", + "median_income = 3.433", + "total_bedrooms = 892.0", + "total_rooms = 3851.0", + "income_cat = 3.0", + "housing_median_age = 11.0" + ], + "yaxis": "y" + } + ], + "layout": { + "annotations": [ + { + "font": { + "size": 16 + }, + "showarrow": false, + "text": "housing RF Pipeline", + "x": 0.5, + "xanchor": "center", + "xref": "paper", + "y": 1, + "yanchor": "bottom", + "yref": "paper" + }, + { + "font": { + "size": 16 + }, + "showarrow": false, + "text": "contribution", + "x": 0.5, + "xanchor": "center", + "xref": "paper", + "y": 0, + "yanchor": "top", + "yref": "paper", + "yshift": -30 + } + ], + "font": { + "color": "#371ea3" + }, + "height": 353, + "margin": { + "b": 71, + "r": 30, + "t": 78 + }, + "shapes": [ + { + "line": { + "color": "#371ea3", + "dash": "dot", + "width": 1.5 + }, + "type": "line", + "x0": 207980.2223998708, + "x1": 207980.2223998708, + "xref": "x", + "y0": -1, + "y1": 10, + "yref": "y" + } + ], + "template": { + "data": { + "scatter": [ + { + "type": "scatter" + } + ] + } + }, + "title": { + "text": "Shapley Values", + "x": 0.15 + }, + "xaxis": { + "anchor": "y", + "automargin": true, + "domain": [ + 0, + 1 + ], + "fixedrange": true, + "gridwidth": 2, + "range": [ + 162414.0274998708, + 233947.8872998708 + ], + "tickcolor": "white", + "ticklen": 3, + "ticks": "outside", + "type": "linear", + "zeroline": false + }, + "yaxis": { + "anchor": "x", + "automargin": true, + "autorange": "reversed", + "domain": [ + 0, + 1 + ], + "fixedrange": true, + "gridwidth": 2, + "tickcolor": "white", + "ticklen": 10, + "ticks": "outside", + "type": "category" + } + } + }, + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# first observation\n", + "model_exp.predict_parts(observation_1.iloc[[0]], \n", + " type = 'break_down',\n", + " order=order).plot()\n", + "model_exp.predict_parts(observation_1.iloc[[0]], \n", + " type = 'shap').plot()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Based on `break_down` plot, the greatest positive inpact on prediction has `total_rooms` and `longitude`. However, `longitude` without `latitude` does not create significant information and we may expect that only interaction of that variables really matters in the model.\n", + "\n", + "The greates negative impact has `ocean_proximity` and `latitude`. Again, only in interaction `latitude` create siginifact infromation.\n", + "\n", + "On `shap` plot, `households`'s impact is positive while on `break_down` it is negative. It may suggest that interaction between `households` and other variables exists. We may expect interaction with `total_*` or `population` variables, as ratio of that variables tell more about housing in the area than single variables." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "displaylogo": false, + "modeBarButtonsToRemove": [ + "sendDataToCloud", + "lasso2d", + "autoScale2d", + "select2d", + "zoom2d", + "pan2d", + "zoomIn2d", + "zoomOut2d", + "resetScale2d", + "toggleSpikelines", + "hoverCompareCartesian", + "hoverClosestCartesian" + ], + "plotlyServerURL": "https://plot.ly", + "staticPlot": false, + "toImageButtonOptions": { + "height": null, + "width": null + } + }, + "data": [ + { + "base": 207980.2223998708, + "connector": { + "line": { + "color": "#371ea3", + "dash": "solid", + "width": 1 + }, + "mode": "spanning" + }, + "decreasing": { + "marker": { + "color": "#f05a71" + } + }, + "hoverinfo": "text+delta", + "hoverlabel": { + "bgcolor": "rgba(0,0,0,0.8)" + }, + "hovertext": [ + "Average response: 207980.222
Prediction: 275726.667", + "longitude = -122.4
increases average response by", + "latitude = 37.73
decreases average response by", + "housing_median_age = 52.0
increases average response by", + "total_rooms = 1142.0
decreases average response by", + "total_bedrooms = 224.0
decreases average response by", + "population = 494.0
increases average response by", + "households = 206.0
decreases average response by", + "median_income = 5.06
increases average response by", + "ocean_proximity = NEAR BAY
increases average response by", + "income_cat = 4.0
decreases average response by", + "Average response: 207980.222
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latitude = 37.73
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# second observation\n", + "model_exp.predict_parts(observation_2, \n", + " type = 'break_down',\n", + " order = order).plot()\n", + "model_exp.predict_parts(observation_2, \n", + " type = 'shap').plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "While in first observation most variables has negative impact, in the second observation they have mainly positive impact. It is worth mentioning that the greatest negative impact in second observation is observed in `households` and `total_rooms` while they have the greatest positive impact in the first observation. The linear positive correlation between `target` and those two variables should be examinated." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since values obtained by `break_down` and `shap` are different, some interactions in model exist. We can easily find them using `break_down_interactions` plots:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "displaylogo": false, + "modeBarButtonsToRemove": [ + "sendDataToCloud", + "lasso2d", + "autoScale2d", + "select2d", + "zoom2d", + "pan2d", + "zoomIn2d", + "zoomOut2d", + "resetScale2d", + "toggleSpikelines", + "hoverCompareCartesian", + "hoverClosestCartesian" + ], + "plotlyServerURL": "https://plot.ly", + "staticPlot": false, + "toImageButtonOptions": { + "height": null, + "width": null + } + }, + "data": [ + { + "base": 207980.2223998708, + "connector": { + "line": { + "color": "#371ea3", + "dash": "solid", + "width": 1 + }, + "mode": "spanning" + }, + "decreasing": { + "marker": { + "color": "#f05a71" + } + }, + "hoverinfo": "text+delta", + "hoverlabel": { + "bgcolor": "rgba(0,0,0,0.8)" + }, + "hovertext": [ + "Average response: 207980.222
Prediction: 137186.667", + "households = 747.0
increases average response by", + "ocean_proximity = INLAND
decreases average response by", + "total_rooms = 3851.0
increases average response by", + "latitude:longitude = 38.68:-121.8
decreases average response by", + "population = 1847.0
decreases average response by", + "median_income = 3.433
decreases average response by", + "housing_median_age = 11.0
increases average response by", + "total_bedrooms = 892.0
decreases average response by", + "income_cat = 3.0
decreases average response by", + "Average response: 207980.222
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model_exp.predict_parts(observation_1, \n", + " type = 'break_down_interactions',\n", + " interaction_preference=1).plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "displaylogo": false, + "modeBarButtonsToRemove": [ + "sendDataToCloud", + "lasso2d", + "autoScale2d", + "select2d", + "zoom2d", + "pan2d", + "zoomIn2d", + "zoomOut2d", + "resetScale2d", + "toggleSpikelines", + "hoverCompareCartesian", + "hoverClosestCartesian" + ], + "plotlyServerURL": "https://plot.ly", + "staticPlot": false, + "toImageButtonOptions": { + "height": null, + "width": null + } + }, + "data": [ + { + "base": 207980.2223998708, + "connector": { + "line": { + "color": "#371ea3", + "dash": "solid", + "width": 1 + }, + "mode": "spanning" + }, + "decreasing": { + "marker": { + "color": "#f05a71" + } + }, + "hoverinfo": "text+delta", + "hoverlabel": { + "bgcolor": "rgba(0,0,0,0.8)" + }, + "hovertext": [ + "Average response: 207980.222
Prediction: 275726.667", + "population = 494.0
increases average response by", + "longitude = -122.4
increases average response by", + "total_bedrooms:total_rooms = 224.0:1142.0
decreases average response by", + "income_cat = 4.0
increases average response by", + "median_income = 5.06
increases average response by", + "ocean_proximity = NEAR BAY
increases average response by", + "housing_median_age = 52.0
increases average response by", + "latitude = 37.73
decreases average response by", + "households = 206.0
decreases average response by", + "Average response: 207980.222
Prediction: 275726.667" + ], + "increasing": { + "marker": { + "color": "#8bdcbe" + } + }, + "measure": [ + "relative", + "relative", + "relative", + "relative", + "relative", + "relative", + "relative", + "relative", + "relative", + "relative", + "total" + ], + "orientation": "h", + "showlegend": false, + "text": [ + "207980.222", + "+48757.659", + "+38111.414", + "-10801.304", + "+26446.031", + "+24238.502", + "+26230.737", + "+29420.153", + "-8543.958", + "-106112.79", + "275726.667" + ], + "textposition": "outside", + "totals": { + "marker": { + "color": "#371ea3" + } + }, + "type": "waterfall", + "x": [ + 0, + 48757.659, + 38111.414, + -10801.304, + 26446.031, + 24238.502, + 26230.737, + 29420.153, + -8543.958, + -106112.79, + 67746.444 + ], + "xaxis": "x", + "y": [ + "intercept", + "population = 494.0", + "longitude = -122.4", + "total_bedrooms:total_rooms = 224.0:1142.0", + "income_cat = 4.0", + "median_income = 5.06", + "ocean_proximity = NEAR BAY", + "housing_median_age = 52.0", + "latitude = 37.73", + "households = 206.0", + "prediction" + ], + "yaxis": "y" + } + ], + "layout": { + "annotations": [ + { + "font": { + "size": 16 + }, + "showarrow": false, + "text": "housing RF Pipeline", + "x": 0.5, + "xanchor": "center", + "xref": "paper", + "y": 1, + "yanchor": "bottom", + "yref": "paper" + }, + { + "font": { + "size": 16 + }, + "showarrow": false, + "text": "contribution", + "x": 0.5, + "xanchor": "center", + "xref": "paper", + "y": 0, + "yanchor": "top", + "yref": "paper", + "yshift": -30 + } + ], + "font": { + "color": "#371ea3" + }, + "height": 373, + "margin": { + "b": 71, + "r": 30, + "t": 78 + }, + "shapes": [ + { + "line": { + "color": "#371ea3", + "dash": "dot", + "width": 1.5 + }, + "type": "line", + "x0": 207980.2223998708, + "x1": 207980.2223998708, + "xref": "x", + "y0": -1, + "y1": 11, + "yref": "y" + } + ], + "template": { + "data": { + "scatter": [ + { + "type": "scatter" + } + ] + } + }, + "title": { + "text": "Break Down", + "x": 0.15 + }, + "xaxis": { + "anchor": "y", + "automargin": true, + "domain": [ + 0, + 1 + ], + "fixedrange": true, + "gridwidth": 2, + "range": [ + 180619.74305000002, + 417743.89395 + ], + "tickcolor": "white", + "ticklen": 3, + "ticks": "outside", + "type": "linear", + "zeroline": false + }, + "yaxis": { + "anchor": "x", + "automargin": true, + "autorange": "reversed", + "domain": [ + 0, + 1 + ], + "fixedrange": true, + "gridwidth": 2, + "tickcolor": "white", + "ticklen": 10, + "ticks": "outside", + "type": "category" + } + } + }, + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model_exp.predict_parts(observation_2, \n", + " type = 'break_down_interactions',\n", + " interaction_preference=1).plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For each observation the interactions are different; however, we can notice that:\n", + "* `latitude` and `longitude` interact with each other;\n", + "* there is interaction between `total_bedrooms` and `total_rooms` - we may expect that their ratio could be informaiton about average size of the houses in the area;" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "7a6c7221b3b3971f6f9ce379d328f92d54aac19d86d8c895482784a858ac35df" + }, + "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": false + }, + "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": 2 +} From 017dcb9bebac5a64b32ee02e0b9f892ceebabaa9 Mon Sep 17 00:00:00 2001 From: DawidPludowski <72541839+DawidPludowski@users.noreply.github.com> Date: Thu, 14 Apr 2022 15:20:18 +0200 Subject: [PATCH 2/5] Pludowski Dawid HW-II --- .../Pludowski_Dawid/pludowski_dawid.Rmd | 70 +++++ .../Pludowski_Dawid/pludowski_dawid.html | 296 ++++++++++++++++++ 2 files changed, 366 insertions(+) create mode 100644 Homeworks/Homework-II/Pludowski_Dawid/pludowski_dawid.Rmd create mode 100644 Homeworks/Homework-II/Pludowski_Dawid/pludowski_dawid.html diff --git a/Homeworks/Homework-II/Pludowski_Dawid/pludowski_dawid.Rmd b/Homeworks/Homework-II/Pludowski_Dawid/pludowski_dawid.Rmd new file mode 100644 index 0000000..211b0f6 --- /dev/null +++ b/Homeworks/Homework-II/Pludowski_Dawid/pludowski_dawid.Rmd @@ -0,0 +1,70 @@ +--- +title: "Homework no. 2" +author: "Dawid Pludowski" +date: "April 10, 2022" +output: + html_document: + df_print: paged +--- + +```{r message=FALSE, warning=FALSE} +library(ranger) +library(DALEX) +library(DALEXtra) +library(lime) + +set.seed(123) + +df <- read.csv2('./../data.csv', sep=',') +df['median_house_value'] <- lapply(df['median_house_value'], FUN = as.integer) + +ranger_model <- ranger(median_house_value ~., data = df) +``` + +## 1. Calculating model prediction + +```{r} +res <- predict(ranger_model, df[2137,])$predictions +cat(res) +``` + +## 2. Calculating LIME decomposition + +```{r message=FALSE} +explainer_rf <- DALEX::explain(ranger_model, + data = df, + y = df$median_house_value, + label = "random forest") + +model_type.dalex_explainer <- DALEXtra::model_type.dalex_explainer +predict_model.dalex_explainer <- DALEXtra::predict_model.dalex_explainer + +lime_pr <- predict_surrogate(explainer = explainer_rf, + new_observation = as.data.frame(df[2137,]), + n_features = 6, + n_permutations = 1000, + type = "lime") + +lime_pr +plot(lime_pr) +``` + +`LIME` decomposition shows that `ocean_proximity` and `total_rooms` have the greatest impact on final prediction. Explanation fit is significantly low, though. + +## 3. Calculating LIME decomposition for different observation + +```{r} +lime_pr <- predict_surrogate(explainer = explainer_rf, + new_observation = as.data.frame(df[420,]), + n_features = 6, + n_permutations = 1000, + type = "lime") + +lime_pr +plot(lime_pr) +``` +As shown in previous homework, `NEAR BAY` value is supposed to have positive impact on model prediction; however, here we obtained negative impact of that value. It may be caused by the fact that in terms of `longitude` and `latitude`, houses near bay has neighbor observations only in one direction. Moreover, explanation fit is really low, which may lead to unstable explanation with that method. + +In both LIME decomposition number of total rooms has similar negative impact of model prediction. There is noticeable difference between impact of `longitude` in each observation, which could be explain be the fact, that little change in distance can change `NEAR BAY` to `<1H OCEAN`, while even great change of that value cannot change `INLAND` into other value. `total_rooms` and `total_bedrooms` seem to have stable impact in neighbors of both observation, maybe because that such a values are equally important independently from other attributes of house. + +In summary, we may expect that some attributes, such as `longitude` or `latitude` are unstable somewhat and other, like `total_rooms` might be much more stable. \ No newline at end of file diff --git a/Homeworks/Homework-II/Pludowski_Dawid/pludowski_dawid.html b/Homeworks/Homework-II/Pludowski_Dawid/pludowski_dawid.html new file mode 100644 index 0000000..b4703a0 --- /dev/null +++ b/Homeworks/Homework-II/Pludowski_Dawid/pludowski_dawid.html @@ -0,0 +1,296 @@ + + + + + + + + + + + + + + + +Homework no. 2 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
library(ranger)
+library(DALEX)
+library(DALEXtra)
+library(lime)
+
+set.seed(123)
+
+df <- read.csv2('./../data.csv', sep=',')
+df['median_house_value'] <- lapply(df['median_house_value'], FUN = as.integer)
+
+ranger_model <- ranger(median_house_value ~., data = df)
+
+

1. Calculating model prediction

+
res <- predict(ranger_model, df[2137,])$predictions
+cat(res)
+
## 277727.6
+
+
+

2. Calculating LIME decomposition

+
explainer_rf <- DALEX::explain(ranger_model, 
+                               data = df,  
+                               y = df$median_house_value,
+                               label = "random forest")
+
## Preparation of a new explainer is initiated
+##   -> model label       :  random forest 
+##   -> data              :  20640  rows  13  cols 
+##   -> target variable   :  20640  values 
+##   -> predict function  :  yhat.ranger  will be used (  default  )
+##   -> predicted values  :  No value for predict function target column. (  default  )
+##   -> model_info        :  package ranger , ver. 0.13.1 , task regression (  default  ) 
+##   -> predicted values  :  numerical, min =  44585.64 , mean =  207152.6 , max =  499971.5  
+##   -> residual function :  difference between y and yhat (  default  )
+##   -> residuals         :  numerical, min =  -145492.8 , mean =  -296.7636 , max =  199630.6  
+##   A new explainer has been created!
+
model_type.dalex_explainer <- DALEXtra::model_type.dalex_explainer
+predict_model.dalex_explainer <- DALEXtra::predict_model.dalex_explainer
+
+lime_pr <- predict_surrogate(explainer = explainer_rf, 
+                             new_observation = as.data.frame(df[2137,]), 
+                             n_features = 6, 
+                             n_permutations = 1000,
+                             type = "lime")
+
+lime_pr
+
+ +
+
plot(lime_pr)
+

+

LIME decomposition shows that ocean_proximity and total_rooms have the greatest impact on final prediction. Explanation fit is significantly low, though.

+
+
+

3. Calculating LIME decomposition for different observation

+
lime_pr <- predict_surrogate(explainer = explainer_rf, 
+                             new_observation = as.data.frame(df[420,]), 
+                             n_features = 6, 
+                             n_permutations = 1000,
+                             type = "lime")
+
+lime_pr
+
+ +
+
plot(lime_pr)
+

As shown in previous homework, NEAR BAY value is supposed to have positive impact on model prediction; however, here we obtained negative impact of that value. It may be caused by the fact that in terms of longitude and latitude, houses near bay has neighbor observations only in one direction. Moreover, explanation fit is really low, which may lead to unstable explanation with that method.

+

In both LIME decomposition number of total rooms has similar negative impact of model prediction. There is noticeable difference between impact of longitude in each observation, which could be explain be the fact, that little change in distance can change NEAR BAY to <1H OCEAN, while even great change of that value cannot change INLAND into other value. total_rooms and total_bedrooms seem to have stable impact in neighbors of both observation, maybe because that such a values are equally important independently from other attributes of house.

+

In summary, we may expect that some attributes, such as longitude or latitude are unstable somewhat and other, like total_rooms might be much more stable.

+
+ + + + +
+ + + + + + + + + + + + + + + From f9e94de204bf521272f16a2e6d05e09020cb532c Mon Sep 17 00:00:00 2001 From: DawidPludowski Date: Tue, 3 May 2022 10:29:17 +0200 Subject: [PATCH 3/5] add hw3 Dawid Pludowski --- .../Pludowski_Dawid/PludowskiDawid.html | 15342 ++++++++++++++++ .../Pludowski_Dawid/PludowskiDawid.ipynb | 10398 +++++++++++ 2 files changed, 25740 insertions(+) create mode 100644 Homeworks/Homework-III/Pludowski_Dawid/PludowskiDawid.html create mode 100644 Homeworks/Homework-III/Pludowski_Dawid/PludowskiDawid.ipynb diff --git a/Homeworks/Homework-III/Pludowski_Dawid/PludowskiDawid.html b/Homeworks/Homework-III/Pludowski_Dawid/PludowskiDawid.html new file mode 100644 index 0000000..45710dc --- /dev/null +++ b/Homeworks/Homework-III/Pludowski_Dawid/PludowskiDawid.html @@ -0,0 +1,15342 @@ + + + + + +PludowskiDawid + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/Homeworks/Homework-III/Pludowski_Dawid/PludowskiDawid.ipynb b/Homeworks/Homework-III/Pludowski_Dawid/PludowskiDawid.ipynb new file mode 100644 index 0000000..51b824b --- /dev/null +++ b/Homeworks/Homework-III/Pludowski_Dawid/PludowskiDawid.ipynb @@ -0,0 +1,10398 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Homework 3\n", + "Author: Dawid Pludowski\n", + "\n", + "* For the selected observation from the dataset, calculate the model prediction.\n", + "* For the selected observation from point 1, calculate the model prediction decomposition using the Ceteris Paribus profiles.\n", + "* Compare the CP profiles of the selected observation on three different models.\n", + "* Comment on the individual results obtained in the above paragraphs." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data preparation" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import pickle as pkl\n", + "import dalex as dx\n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Most data preprocessing was done for purpose of previous homeworks; only `python`-wise preprocessing, such as managing with categories, is required." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('../data_scaled.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomerooms_per_householdbedrooms_per_roompopulation_per_householdocean_proximitymedian_house_value
0-1.3278351.0525480.982143-0.804819-0.972476-0.974429-0.9770332.3447660.628559-1.149930-0.049597NEAR BAY452600.0
1-1.3228441.043185-0.6070192.0458901.3571430.8614391.6699612.3322380.327041-0.990381-0.092512NEAR BAY358500.0
2-1.3328271.0385031.856182-0.535746-0.827024-0.820777-0.8436371.7826991.155620-1.445865-0.025843NEAR BAY352100.0
3-1.3378181.0385031.856182-0.624215-0.719723-0.766028-0.7337810.9329680.156966-0.493627-0.050329NEAR BAY341300.0
4-1.3378181.0385031.856182-0.462404-0.612423-0.759847-0.629157-0.0128810.344711-0.707889-0.085616NEAR BAY342200.0
\n", + "
" + ], + "text/plain": [ + " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", + "0 -1.327835 1.052548 0.982143 -0.804819 -0.972476 \n", + "1 -1.322844 1.043185 -0.607019 2.045890 1.357143 \n", + "2 -1.332827 1.038503 1.856182 -0.535746 -0.827024 \n", + "3 -1.337818 1.038503 1.856182 -0.624215 -0.719723 \n", + "4 -1.337818 1.038503 1.856182 -0.462404 -0.612423 \n", + "\n", + " population households median_income rooms_per_household \\\n", + "0 -0.974429 -0.977033 2.344766 0.628559 \n", + "1 0.861439 1.669961 2.332238 0.327041 \n", + "2 -0.820777 -0.843637 1.782699 1.155620 \n", + "3 -0.766028 -0.733781 0.932968 0.156966 \n", + "4 -0.759847 -0.629157 -0.012881 0.344711 \n", + "\n", + " bedrooms_per_room population_per_household ocean_proximity \\\n", + "0 -1.149930 -0.049597 NEAR BAY \n", + "1 -0.990381 -0.092512 NEAR BAY \n", + "2 -1.445865 -0.025843 NEAR BAY \n", + "3 -0.493627 -0.050329 NEAR BAY \n", + "4 -0.707889 -0.085616 NEAR BAY \n", + "\n", + " median_house_value \n", + "0 452600.0 \n", + "1 358500.0 \n", + "2 352100.0 \n", + "3 341300.0 \n", + "4 342200.0 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "mapping = {\n", + " 'NEAR BAY': 0,\n", + " 'ISLAND': 1,\n", + " 'NEAR OCEAN': 2,\n", + " '<1H OCEAN': 3,\n", + " 'INLAND': 4\n", + "}\n", + "\n", + "df['ocean_proximity'] = df['ocean_proximity'].map(mapping)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "X = df.drop(columns=['median_house_value'])\n", + "y = df[['median_house_value']]\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating models" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.tree import DecisionTreeRegressor\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "from sklearn.neural_network import MLPRegressor\n", + "\n", + "from sklearn.model_selection import RandomizedSearchCV" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Decision tree" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "dt = DecisionTreeRegressor(max_depth=10)\n", + "dt_tuned = RandomizedSearchCV(\n", + " dt,\n", + " {\n", + " 'criterion': ['squared_error', 'absolute_error'],\n", + " 'max_depth': [i for i in range(5, 25, 2)],\n", + " 'min_samples_split': [i for i in range(2, 10)],\n", + " 'min_samples_leaf': [i for i in range(1, 5)]\n", + " },\n", + " n_iter=15,\n", + " random_state=2137\n", + ")\n", + "\n", + "\n", + "dt_tuned.fit(X_train, y_train)\n", + "print(dt_tuned.best_estimator_)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# with open('decision_tree.pkl', 'rb') as file:\n", + "# dt_tuned = pkl.load(file)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Random forest" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "rf = RandomForestRegressor()\n", + "rf_tuned = RandomizedSearchCV(\n", + " dt,\n", + " {\n", + " 'criterion': ['squared_error', 'absolute_error'],\n", + " 'max_features': ['sqrt', 'log2'],\n", + " 'min_samples_split': [i for i in range(2, 10)],\n", + " 'min_samples_leaf': [i for i in range(1, 5)],\n", + " 'max_depth': [i for i in range(3, 10, 2)]\n", + " },\n", + " n_iter=15,\n", + " random_state=2137\n", + ")\n", + "\n", + "rf_tuned.fit(X_train, y_train)\n", + "print(rf_tuned.best_estimator_)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# with open('random_forest.pkl', 'rb') as file:\n", + "# rf_tuned = pkl.load(file)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Neural network" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "mlp = MLPRegressor(\n", + " random_state=2137\n", + ")\n", + "\n", + "mlp_tuned = RandomizedSearchCV(\n", + " mlp,\n", + " {\n", + " 'hidden_layer_sizes': [\n", + " (10, 100, 20),\n", + " (5, 50, 50, 10),\n", + " (25, 100, 20)\n", + " ]\n", + " },\n", + " n_iter=3\n", + ")\n", + "\n", + "mlp_tuned.fit(X_train, y_train)\n", + "print(mlp_tuned.best_estimator_)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# with open('mlp.pkl', 'rb') as file:\n", + "# mlp_tuned = pkl.load(file)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Choosing best model" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "decision tree score: 0.7122037298977866\n", + "random forest score: 0.6655753723827369\n", + "neural network score: 0.7030360456509592\n" + ] + } + ], + "source": [ + "print(f'decision tree score: {dt_tuned.score(X_test, y_test)}')\n", + "print(f'random forest score: {rf_tuned.score(X_test, y_test)}')\n", + "print(f'neural network score: {mlp_tuned.score(X_test, y_test)}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Decision tree's performance get best scoring, so we will consider it as base model in the following part of the notebook." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Homework p.1" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomerooms_per_householdbedrooms_per_roompopulation_per_householdocean_proximitymedian_house_value
2137-0.0750170.551589-1.083767-0.2112080.064765-0.204406-0.045877-0.62848-0.3704570.803675-0.057143487500.0
\n", + "
" + ], + "text/plain": [ + " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", + "2137 -0.075017 0.551589 -1.083767 -0.211208 0.064765 \n", + "\n", + " population households median_income rooms_per_household \\\n", + "2137 -0.204406 -0.045877 -0.62848 -0.370457 \n", + "\n", + " bedrooms_per_room population_per_household ocean_proximity \\\n", + "2137 0.803675 -0.057143 4 \n", + "\n", + " median_house_value \n", + "2137 87500.0 " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "observation = df.iloc[[2137]]\n", + "observation" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "true value: 87500\n", + "predicted value: 79850.0\n" + ] + } + ], + "source": [ + "prediction = dt_tuned.predict(observation.drop(columns=['median_house_value']))\n", + "true_value = observation['median_house_value']\n", + "\n", + "print(f'true value: {int(true_value)}')\n", + "print(f'predicted value: {prediction[0]}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Predicted value is close to real one." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Homework p.2" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Preparation of a new explainer is initiated\n", + "\n", + " -> data : 20640 rows 12 cols\n", + " -> target variable : Parameter 'y' was a pandas.DataFrame. Converted to a numpy.ndarray.\n", + " -> target variable : 20640 values\n", + " -> model_class : sklearn.model_selection._search.RandomizedSearchCV (default)\n", + " -> label : decision tree\n", + " -> predict function : will be used (default)\n", + " -> predict function : Accepts pandas.DataFrame and numpy.ndarray.\n", + " -> predicted values : min = 3.62e+04, mean = 2.02e+05, max = 5e+05\n", + " -> model type : regression will be used (default)\n", + " -> residual function : difference between y and yhat (default)\n", + " -> residuals : min = -3.88e+05, mean = 4.6e+03, max = 4.31e+05\n", + " -> model_info : package sklearn\n", + "\n", + "A new explainer has been created!\n" + ] + } + ], + "source": [ + "dt_explainer = dx.Explainer(\n", + " dt_tuned,\n", + " X,\n", + " y,\n", + " label='decision tree'\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Calculating ceteris paribus: 100%|████████████████████████████████████████████████████| 12/12 [00:00<00:00, 398.05it/s]\n" + ] + } + ], + "source": [ + "dt_observation_exp = dt_explainer.predict_profile(observation.drop(columns=['median_house_value']))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "displaylogo": false, + "modeBarButtonsToRemove": [ + "sendDataToCloud", + "lasso2d", + "autoScale2d", + "select2d", + "zoom2d", + "pan2d", + "zoomIn2d", + "zoomOut2d", + "resetScale2d", + "toggleSpikelines", + "hoverCompareCartesian", + "hoverClosestCartesian" + ], + "plotlyServerURL": "https://plot.ly", + "staticPlot": false, + "toImageButtonOptions": { + "height": null, + "width": null + } + }, + "data": [ + { + "customdata": [ + [ + "
id: 2137
prediction: 106500.0
median_income: -1.7742994673175232

longitude: -0.075017121957829
latitude: 0.5515886536187085
housing_median_age: -1.0837673848399594
total_rooms: -0.2112078394038592
total_bedrooms: 0.0647647292141163
population: -0.2044062643863409
households: -0.045876903415042
rooms_per_household: -0.3704566179695522
bedrooms_per_room: 0.8036748508705596
population_per_household: -0.0571431043175883
ocean_proximity: 4.0
... too many variables" + ], + [ + "
id: 2137
prediction: 106500.0
median_income: -1.6979736145265452

longitude: -0.075017121957829
latitude: 0.5515886536187085
housing_median_age: -1.0837673848399594
total_rooms: -0.2112078394038592
total_bedrooms: 0.0647647292141163
population: -0.2044062643863409
households: -0.045876903415042
rooms_per_household: -0.3704566179695522
bedrooms_per_room: 0.8036748508705596
population_per_household: -0.0571431043175883
ocean_proximity: 4.0
... too many variables" + ], + [ + "
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dt_observation_exp.plot(\n", + " variables=['median_income', 'ocean_proximity', 'households', 'housing_median_age']\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Despite the highest scoring, decision tree's decisions are based only on 2-3 variables out of 12 (rest of plots are not shown for sake of notebook clarity). It may suggest that this kind of model cannot use full information that is hidden in data and thus, other models should be considered. \n", + "\n", + "The greatest impact on the prediciton has `median_income` and it follows the rule *the richer inhabitants are, the more expensive the neighbourhood is*, which is reasonable. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Homework p.3" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Preparation of a new explainer is initiated\n", + "\n", + " -> data : 20640 rows 12 cols\n", + " -> target variable : Parameter 'y' was a pandas.DataFrame. Converted to a numpy.ndarray.\n", + " -> target variable : 20640 values\n", + " -> model_class : sklearn.model_selection._search.RandomizedSearchCV (default)\n", + " -> label : random forest\n", + " -> predict function : will be used (default)\n", + " -> predict function : Accepts pandas.DataFrame and numpy.ndarray.\n", + " -> predicted values : min = 5.24e+04, mean = 1.99e+05, max = 5e+05\n", + " -> model type : regression will be used (default)\n", + " -> residual function : difference between y and yhat (default)\n", + " -> residuals : min = -3.58e+05, mean = 8.16e+03, max = 3.96e+05\n", + " -> model_info : package sklearn\n", + "\n", + "A new explainer has been created!\n", + "Preparation of a new explainer is initiated\n", + "\n", + " -> data : 20640 rows 12 cols\n", + " -> target variable : Parameter 'y' was a pandas.DataFrame. Converted to a numpy.ndarray.\n", + " -> target variable : 20640 values\n", + " -> model_class : sklearn.model_selection._search.RandomizedSearchCV (default)\n", + " -> label : neural network\n", + " -> predict function : will be used (default)\n", + " -> predict function : Accepts pandas.DataFrame and numpy.ndarray.\n", + " -> predicted values : min = 3.14e+04, mean = 2.08e+05, max = 7.78e+05\n", + " -> model type : regression will be used (default)\n", + " -> residual function : difference between y and yhat (default)\n", + " -> residuals : min = -6.11e+05, mean = -1.2e+03, max = 4.2e+05\n", + " -> model_info : package sklearn\n", + "\n", + "A new explainer has been created!\n" + ] + } + ], + "source": [ + "rf_explainer = dx.Explainer(\n", + " rf_tuned,\n", + " X,\n", + " y,\n", + " label='random forest'\n", + ")\n", + "\n", + "mlp_expaliner = dx.Explainer(\n", + " mlp_tuned,\n", + " X,\n", + " y,\n", + " label='neural network'\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Calculating ceteris paribus: 100%|████████████████████████████████████████████████████| 12/12 [00:00<00:00, 428.69it/s]\n", + "Calculating ceteris paribus: 100%|████████████████████████████████████████████████████| 12/12 [00:00<00:00, 387.16it/s]\n" + ] + } + ], + "source": [ + "rf_observation_exp = rf_explainer.predict_profile(observation.drop(columns=['median_house_value']))\n", + "mlp_observation_exp = mlp_expaliner.predict_profile(observation.drop(columns=['median_house_value']))" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "displaylogo": false, + "modeBarButtonsToRemove": [ + "sendDataToCloud", + "lasso2d", + "autoScale2d", + "select2d", + "zoom2d", + "pan2d", + "zoomIn2d", + "zoomOut2d", + "resetScale2d", + "toggleSpikelines", + "hoverCompareCartesian", + "hoverClosestCartesian" + ], + "plotlyServerURL": "https://plot.ly", + "staticPlot": false, + "toImageButtonOptions": { + "height": null, + "width": null + } + }, + "data": [ + { + "customdata": [ + [ + "
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dt_observation_exp.plot((rf_observation_exp, mlp_observation_exp), variables=['median_income', 'ocean_proximity', 'households', 'housing_median_age'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The models comparsion in that certain observation shows that neural network might be more sensitive on changes in `households` variables. Moreover, full CP plots (not shown in the notebook) suggest that change in any variable has impact on network decision, whilst it is not true for random forest and decision tree. Further analysis should be performed to check whether neural network changes in CP profiles are reasonable; if so, neural network should be considered as the best model to estimate **median price**, as its scoring is only a bit lower than in decision tree and its prediction is more subtle." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "CP plots show that the best model (in terms of scoring) may not take all information into account and due to that fact, be poor explainer of the real world. However, one should remember that dataset do contain some interactions (like `longitute` and `latitude`) and correlation (ratio variables) and because of that, CP plots are not methods to explain model performance." + ] + } + ], + "metadata": { + "interpreter": { + "hash": "c00db682d3d9df7c3222c4a616bdb112dd6ae16ee33e6230796e3de211819f1a" + }, + "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": false + }, + "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": 2 +} From 67a61607ec032547a1ba706f1e0d68ad6dee50ab Mon Sep 17 00:00:00 2001 From: DawidPludowski <72541839+DawidPludowski@users.noreply.github.com> Date: Thu, 26 May 2022 21:56:53 +0200 Subject: [PATCH 4/5] add hw IV --- .../DawidPludowski-IV.html | 14512 ++++++++++++++++ 1 file changed, 14512 insertions(+) create mode 100644 Homeworks/Homework-IV/Pludowski-Dawid-HW-IV/DawidPludowski-IV.html diff --git a/Homeworks/Homework-IV/Pludowski-Dawid-HW-IV/DawidPludowski-IV.html b/Homeworks/Homework-IV/Pludowski-Dawid-HW-IV/DawidPludowski-IV.html new file mode 100644 index 0000000..aa195c9 --- /dev/null +++ b/Homeworks/Homework-IV/Pludowski-Dawid-HW-IV/DawidPludowski-IV.html @@ -0,0 +1,14512 @@ + + + + + +DawidPludowski-IV + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + From bb778f9ef19a39d5666bfef8d8c11b5c8cee2f20 Mon Sep 17 00:00:00 2001 From: DawidPludowski <72541839+DawidPludowski@users.noreply.github.com> Date: Thu, 26 May 2022 21:57:48 +0200 Subject: [PATCH 5/5] add hw V --- Homeworks/Homework-V/DawidPludowski-V.html | 14706 +++++++++++++++++++ 1 file changed, 14706 insertions(+) create mode 100644 Homeworks/Homework-V/DawidPludowski-V.html diff --git a/Homeworks/Homework-V/DawidPludowski-V.html b/Homeworks/Homework-V/DawidPludowski-V.html new file mode 100644 index 0000000..be6b010 --- /dev/null +++ b/Homeworks/Homework-V/DawidPludowski-V.html @@ -0,0 +1,14706 @@ + + + + + +DawidPludowski-V + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +