From 013880b821e97921f2ac478636464e26b2382fd0 Mon Sep 17 00:00:00 2001 From: Alexander Ledovsky Date: Fri, 11 Sep 2020 15:50:05 +0200 Subject: [PATCH] Updated hw-1 --- seminar1/hw1-baseline.ipynb | 107 ++++++++++++++++----------------- seminar1/sample_submission.csv | 2 +- 2 files changed, 52 insertions(+), 57 deletions(-) diff --git a/seminar1/hw1-baseline.ipynb b/seminar1/hw1-baseline.ipynb index ef23bac..b3eced5 100644 --- a/seminar1/hw1-baseline.ipynb +++ b/seminar1/hw1-baseline.ipynb @@ -25,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -47,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -255,7 +255,7 @@ "[5 rows x 22 columns]" ] }, - "execution_count": 4, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -266,7 +266,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -275,7 +275,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -284,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -306,7 +306,7 @@ " 279, 281, 283, 285, 287, 290, 292, 293, 294, 297, 298])" ] }, - "execution_count": 7, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -317,7 +317,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -334,16 +334,16 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 9, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" }, @@ -366,16 +366,16 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 10, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" }, @@ -398,7 +398,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -440,7 +440,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 154, "metadata": {}, "outputs": [], "source": [ @@ -453,7 +453,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 155, "metadata": {}, "outputs": [], "source": [ @@ -463,7 +463,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 156, "metadata": {}, "outputs": [], "source": [ @@ -474,7 +474,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 157, "metadata": {}, "outputs": [], "source": [ @@ -483,7 +483,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 167, "metadata": {}, "outputs": [ { @@ -492,7 +492,7 @@ "LogisticRegression(C=1)" ] }, - "execution_count": 16, + "execution_count": 167, "metadata": {}, "output_type": "execute_result" } @@ -503,7 +503,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 168, "metadata": {}, "outputs": [ { @@ -512,7 +512,7 @@ "0.7982700892857142" ] }, - "execution_count": 17, + "execution_count": 168, "metadata": {}, "output_type": "execute_result" } @@ -524,25 +524,25 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 169, "metadata": {}, "outputs": [], "source": [ - "y_pred = model.predict_proba(X_test)[:, 1]" + "y_pred = model.predict_proba(X_test_sc)[:, 1]" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 170, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.7251131221719458" + "0.7171945701357466" ] }, - "execution_count": 19, + "execution_count": 170, "metadata": {}, "output_type": "execute_result" } @@ -560,7 +560,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 171, "metadata": {}, "outputs": [], "source": [ @@ -569,22 +569,22 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 172, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 21, + "execution_count": 172, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -609,7 +609,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 227, "metadata": {}, "outputs": [], "source": [ @@ -619,7 +619,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 228, "metadata": {}, "outputs": [ { @@ -628,68 +628,63 @@ "LogisticRegression(C=1)" ] }, - "execution_count": 23, + "execution_count": 228, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "model.fit(X, y)" + "model.fit(X_sc, y)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 229, "metadata": {}, "outputs": [], "source": [ - "X_test = calc_features(df_train)\n", + "X_test = calc_features(df_test)\n", "submission = X_test[['epoch']].copy()\n", - "del X_test['epoch']" + "del X_test['epoch']\n", + "X_test_sc = scaler.transform(X_test)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 230, "metadata": {}, "outputs": [], "source": [ - "y_pred = model.predict_proba(X_test)[:, 1]" + "y_pred = model.predict_proba(X_test_sc)[:, 1]" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 231, "metadata": {}, "outputs": [], "source": [ - "submission['y_pred'] = y_pred" + "submission['Predicted'] = y_pred" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 232, "metadata": {}, "outputs": [], "source": [ - "submission.head()" + "submission['Id'] = submission['epoch']\n", + "del submission['epoch']" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 233, "metadata": {}, "outputs": [], "source": [ - "submission.to_csv('sample_submission.csv', index=False)" + "submission.to_csv('baseline_submission.csv', index=False)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/seminar1/sample_submission.csv b/seminar1/sample_submission.csv index 87c0285..868efc8 100644 --- a/seminar1/sample_submission.csv +++ b/seminar1/sample_submission.csv @@ -1,4 +1,4 @@ -epoch,y_pred +epoch,Predicted 0,0.5175062935008798 2,0.5712788336333584 6,0.9509943662953485