{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import mean_squared_error\n", "import matplotlib.pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cel: rozpoznanie średniej oceny użytkowników dla danego filmu na bazie:\n", "- roku wydania\n", "- gatunku\n", "- czasu trwania filmu\n", "- ilości głosów\n", "- oceny krytyków (metascore)\n", "- przychodu" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 0. Preprocessing" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
yeardurationavg_votevotesworlwide_gross_incomemetascoreActionAdventureAnimationBiography...HorrorMusicMusicalMysteryRomanceSci-FiSportThrillerWarWestern
50619271538.31560761349711.098.00000...0000010000
6281928728.12741426916.090.00000...0000100000
85619301047.7133114410.088.00000...0100000000
10481931878.516266846008.099.00000...0000100000
10861931707.8633151626.091.00000...1000100000
\n", "

5 rows × 27 columns

\n", "
" ], "text/plain": [ " year duration avg_vote votes worlwide_gross_income metascore \\\n", "506 1927 153 8.3 156076 1349711.0 98.0 \n", "628 1928 72 8.1 27414 26916.0 90.0 \n", "856 1930 104 7.7 13311 4410.0 88.0 \n", "1048 1931 87 8.5 162668 46008.0 99.0 \n", "1086 1931 70 7.8 63315 1626.0 91.0 \n", "\n", " Action Adventure Animation Biography ... Horror Music Musical \\\n", "506 0 0 0 0 ... 0 0 0 \n", "628 0 0 0 0 ... 0 0 0 \n", "856 0 0 0 0 ... 0 1 0 \n", "1048 0 0 0 0 ... 0 0 0 \n", "1086 0 0 0 0 ... 1 0 0 \n", "\n", " Mystery Romance Sci-Fi Sport Thriller War Western \n", "506 0 0 1 0 0 0 0 \n", "628 0 1 0 0 0 0 0 \n", "856 0 0 0 0 0 0 0 \n", "1048 0 1 0 0 0 0 0 \n", "1086 0 1 0 0 0 0 0 \n", "\n", "[5 rows x 27 columns]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data = pd.read_csv('IMDb movies.csv', low_memory=False)\n", "data = data[[\"year\",\"genre\", \"duration\", \"avg_vote\", \"votes\", \"worlwide_gross_income\", \"metascore\"]]\n", "data = data.dropna()\n", "data = data[~data[\"worlwide_gross_income\"].str.contains(\"NPR\")]\n", "data[\"worlwide_gross_income\"] = data[\"worlwide_gross_income\"].str.replace('$ ','', regex=False).astype(float)\n", "data[\"genre\"] = data[\"genre\"].str.split(\", \")\n", "genres = pd.get_dummies(data[\"genre\"].apply(pd.Series).stack()).sum(level=0)\n", "data = pd.concat([data.drop(columns=[\"genre\"]), genres.reindex(data.index)], axis=1)\n", "display(data.head(5))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "X = data.drop(columns=[\"avg_vote\"])\n", "X[\"votes\"] = X[\"votes\"]/data[\"votes\"].max()\n", "X[\"duration\"] = X[\"duration\"]/data[\"duration\"].max()\n", "X[\"worlwide_gross_income\"] = X[\"worlwide_gross_income\"]/data[\"worlwide_gross_income\"].max()\n", "X[\"metascore\"] = X[\"metascore\"]/data[\"metascore\"].max()\n", "X[\"year\"] = X[\"year\"].astype(int)\n", "X[\"year\"] = X[\"year\"]/X[\"year\"].max()\n", "\n", "Y = data[\"avg_vote\"]/10" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, train_size=0.8)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1. Regresja liniowa" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test: 0.0033762327444214367\n", "Train: 0.0036015583726998865\n" ] } ], "source": [ "from sklearn.linear_model import LinearRegression\n", "\n", "linear_model = LinearRegression()\n", "linear_model.fit(X_train,Y_train)\n", "\n", "Y_linear_test_pred = linear_model.predict(X_test)\n", "Y_linear_train_pred = linear_model.predict(X_train)\n", "linear_mean_squared = mean_squared_error(Y_test, Y_linear_test_pred)\n", "linear_mean_squared_train = mean_squared_error(Y_train, Y_linear_train_pred)\n", "\n", "print(f\"Test: {linear_mean_squared}\")\n", "print(f\"Train: {linear_mean_squared_train}\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.0, 1.0)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Średnia głosów na podstawie metascore\n", "# niebieski kolor oznacza faktyczny stosunek a zielony stosunek oszacowany przez model\n", "\n", "fig = plt.figure(figsize=(10,5))\n", "chart = fig.add_subplot()\n", "chart.plot(X_test[\"metascore\"], Y_test,\"bo\")\n", "chart.plot(X_test[\"metascore\"], Y_linear_test_pred, \"go\")\n", "plt.ylim([0,1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2.1. Regresja wielomianowa" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "degree = 3" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(degree=3)),\n", " ('linearregression', LinearRegression())])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.pipeline import make_pipeline\n", "from sklearn.preprocessing import PolynomialFeatures\n", "from sklearn.linear_model import Ridge\n", "\n", "polynomial_model = make_pipeline(PolynomialFeatures(degree=degree, include_bias=True), \n", " LinearRegression())\n", "polynomial_model.fit(X_train,Y_train)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test: 0.08647485505065261\n", "Train: 0.0024716850438651133\n" ] } ], "source": [ "Y_polynomial_test_pred = polynomial_model.predict(X_test)\n", "Y_polynomial_train_pred = polynomial_model.predict(X_train)\n", "\n", "polynomial_mean_squared = mean_squared_error(Y_test, Y_polynomial_test_pred)\n", "polynomial_mean_squared_train = mean_squared_error(Y_train, Y_polynomial_train_pred)\n", "\n", "print(f\"Test: {polynomial_mean_squared}\")\n", "print(f\"Train: {polynomial_mean_squared_train}\")" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test: 0.007654936380156268\n", "Train: 0.0024716850438651133\n" ] } ], "source": [ "#Funkcja skokowa Heaviside’a\n", "Y_normalized_polynomial_test_pred = []\n", "Y_normalized_polynomial_train_pred = []\n", "\n", "for x in Y_polynomial_test_pred:\n", " x = min(x,1)\n", " x = max(0, x)\n", " Y_normalized_polynomial_test_pred.append(x)\n", " \n", "for x in Y_polynomial_train_pred:\n", " x = min(x,1)\n", " x = max(0, x)\n", " Y_normalized_polynomial_train_pred.append(x)\n", " \n", "polynomial_normalized_mean_squared = mean_squared_error(Y_test, Y_normalized_polynomial_test_pred)\n", "polynomial_normalized_mean_squared_train = mean_squared_error(Y_train, Y_normalized_polynomial_train_pred)\n", "\n", "print(f\"Test: {polynomial_normalized_mean_squared}\")\n", "print(f\"Train: {polynomial_normalized_mean_squared_train}\")" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.0, 1.0)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Średnia głosów na podstawie metascore\n", "# niebieski kolor oznacza faktyczny stosunek a zielony stosunek oszacowany przez model\n", "\n", "fig = plt.figure(figsize=(10,5))\n", "chart = fig.add_subplot()\n", "chart.plot(X_test[\"metascore\"], Y_test,\"bo\")\n", "chart.plot(X_test[\"metascore\"], Y_normalized_polynomial_test_pred, \"go\")\n", "plt.ylim([0,1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2.2 Regresja wielomianowa z regularyzacją" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "polynomial_regular_model = make_pipeline(PolynomialFeatures(degree=degree, include_bias=True),\n", " Ridge(alpha=10, fit_intercept=True))\n", "polynomial_regular_model.fit(X_train,Y_train)\n", "\n", "Y_polynomial_regular_test_pred = polynomial_regular_model.predict(X_test)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test: 0.003350267646086885\n", "Train: 0.0033181075895871736\n" ] } ], "source": [ "Y_polynomial_regular_train_pred = polynomial_regular_model.predict(X_train)\n", "\n", "polynomial_regular_mean_squared = mean_squared_error(Y_test, Y_polynomial_regular_test_pred)\n", "polynomial_regular_mean_squared_train = mean_squared_error(Y_train, Y_polynomial_regular_train_pred)\n", "\n", "print(f\"Test: {polynomial_regular_mean_squared}\")\n", "print(f\"Train: {polynomial_regular_mean_squared_train}\")" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.0, 1.0)" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Średnia głosów na podstawie metascore\n", "# niebieski kolor oznacza faktyczny stosunek a zielony stosunek oszacowany przez model\n", "\n", "fig = plt.figure(figsize=(10,5))\n", "chart = fig.add_subplot()\n", "chart.plot(X_test[\"metascore\"], Y_test,\"bo\")\n", "chart.plot(X_test[\"metascore\"], Y_polynomial_regular_test_pred, \"go\")\n", "plt.ylim([0,1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3. Sieć neuronowa" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "from tensorflow import keras\n", "from tensorflow.keras import layers\n", "\n", "batch_size = 16\n", "epochs = 10\n", "\n", "model_nn = keras.Sequential(name=\"movies\")\n", "model_nn.add(keras.Input(shape=(26,), name=\"input\"))\n", "model_nn.add(layers.Dense(12, activation=\"relu\", name=\"layer1\"))\n", "model_nn.add(layers.Dense(8, activation=\"sigmoid\", name=\"layer2\"))\n", "model_nn.add(layers.Dense(1, activation=\"softplus\", name=\"output\"))\n", "\n", "model_nn.compile(\n", " loss='mean_squared_error'\n", ")\n", "\n", "model_nn.fit(\n", " X_train.to_numpy().astype(float),\n", " Y_train.to_numpy(),\n", " batch_size=batch_size,\n", " epochs=epochs,\n", " verbose=0\n", ")" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test: 0.0034861018261550737\n", "Train: 0.003624740580524968\n" ] } ], "source": [ "Y_nn_test_pred = model_nn.predict(X_test.to_numpy().astype(float))\n", "Y_nn_train_pred = model_nn.predict(X_train.to_numpy().astype(float))\n", "\n", "nn_mean_squared = mean_squared_error(Y_test, Y_nn_test_pred)\n", "nn_mean_squared_train = mean_squared_error(Y_train, Y_nn_train_pred)\n", "print(f\"Test: {nn_mean_squared}\")\n", "print(f\"Train: {nn_mean_squared_train}\")" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.0, 1.0)" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Średnia głosów na podstawie metascore\n", "# niebieski kolor oznacza faktyczny stosunek a zielony stosunek oszacowany przez model\n", "\n", "fig = plt.figure(figsize=(10,5))\n", "chart = fig.add_subplot()\n", "chart.plot(X_test[\"metascore\"], Y_test,\"bo\")\n", "chart.plot(X_test[\"metascore\"], Y_nn_test_pred, \"go\")\n", "plt.ylim([0,1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Podsumowanie" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(10,5))\n", "chart = fig.add_subplot()\n", "chart.plot(Y_nn_test_pred, Y_test,\"bo\", alpha=0.5, label='Sieć neuronowa')\n", "chart.plot(Y_polynomial_test_pred, Y_test,\"ro\", alpha=0.5, label=f'Regresja wielomianowa (stopnia {degree})')\n", "chart.plot(Y_polynomial_regular_test_pred, Y_test,\"yo\", alpha=0.5, label=f'Regresja wielomianowa z wygładzaniem (stopnia {degree})')\n", "chart.plot(Y_linear_test_pred, Y_test,\"go\", alpha=0.5, label='Regresja liniowa')\n", "\n", "plt.title('Stosunek rozpoznego Y do prawidłowego Y')\n", "plt.ylim([0,1])\n", "plt.xlim([0,1])\n", "\n", "chart.legend()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
NazwaMean squared error (train)Mean squared error (test)
0Regresja liniowa0.0036020.003376
1Regresja wielomianowa (stopień 3)0.0024720.086475
2Regresja wielomianowa z funkcją skokową Heavis...0.0024720.007655
3Regresja wielomianowa z regularyzjacją (stopie...0.0033180.003350
4Sieć neuronowa0.0036250.003486
\n", "
" ], "text/plain": [ " Nazwa \\\n", "0 Regresja liniowa \n", "1 Regresja wielomianowa (stopień 3) \n", "2 Regresja wielomianowa z funkcją skokową Heavis... \n", "3 Regresja wielomianowa z regularyzjacją (stopie... \n", "4 Sieć neuronowa \n", "\n", " Mean squared error (train) Mean squared error (test) \n", "0 0.003602 0.003376 \n", "1 0.002472 0.086475 \n", "2 0.002472 0.007655 \n", "3 0.003318 0.003350 \n", "4 0.003625 0.003486 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dict = {'Nazwa' : ['Regresja liniowa', f'Regresja wielomianowa (stopień {degree})', f'Regresja wielomianowa z funkcją skokową Heaviside\\'a (stopień {degree})', f'Regresja wielomianowa z regularyzjacją (stopień {degree})', 'Sieć neuronowa'],\n", " 'Mean squared error (train)' : [linear_mean_squared_train, polynomial_mean_squared_train, polynomial_normalized_mean_squared_train, polynomial_regular_mean_squared_train, nn_mean_squared_train],\n", " 'Mean squared error (test)' : [linear_mean_squared, polynomial_mean_squared, polynomial_normalized_mean_squared, polynomial_regular_mean_squared, nn_mean_squared]}\n", "df = pd.DataFrame(dict)\n", "display(df)" ] } ], "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" } }, "nbformat": 4, "nbformat_minor": 4 }