diff --git a/HW 5/Iris_knn_W2025_template.ipynb b/HW 5/Iris_knn_W2025_template.ipynb index d128892..a443351 100644 --- a/HW 5/Iris_knn_W2025_template.ipynb +++ b/HW 5/Iris_knn_W2025_template.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 7, "metadata": { "id": "Q6ifg03dKPR4" }, @@ -19,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 8, "metadata": { "id": "0ghtg7ecRQ50" }, @@ -40,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -107,13 +107,6 @@ " 0.2\n", " \n", " \n", - " 4\n", - " 5.0\n", - " 3.6\n", - " 1.4\n", - " 0.2\n", - " \n", - " \n", " ...\n", " ...\n", " ...\n", @@ -121,13 +114,6 @@ " ...\n", " \n", " \n", - " 145\n", - " 6.7\n", - " 3.0\n", - " 5.2\n", - " 2.3\n", - " \n", - " \n", " 146\n", " 6.3\n", " 2.5\n", @@ -166,9 +152,7 @@ "1 4.9 3.0 1.4 0.2\n", "2 4.7 3.2 1.3 0.2\n", "3 4.6 3.1 1.5 0.2\n", - "4 5.0 3.6 1.4 0.2\n", ".. ... ... ... ...\n", - "145 6.7 3.0 5.2 2.3\n", "146 6.3 2.5 5.0 1.9\n", "147 6.5 3.0 5.2 2.0\n", "148 6.2 3.4 5.4 2.3\n", @@ -177,7 +161,7 @@ "[150 rows x 4 columns]" ] }, - "execution_count": 4, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -185,14 +169,14 @@ "source": [ "####################################\n", "from sklearn.datasets import load_iris\n", - "data = load_iris()\n", + "#data = load_iris()\n", "df = pd.DataFrame(data.data, columns=data.feature_names)\n", "df" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 13, "metadata": { "id": "ANJhDRNPKPR5" }, @@ -292,7 +276,7 @@ "4 5.0 3.6 1.4 0.2 setosa remove_me" ] }, - "execution_count": 5, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -312,7 +296,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 14, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -343,19 +327,23 @@ " \n", " \n", " \n", - " sepal length (cm)\n", - " sepal width (cm)\n", - " petal length (cm)\n", - " petal width (cm)\n", + " sepallen\n", + " sepalwid\n", + " petallen\n", + " petalwid\n", + " irisname\n", + " junk\n", " \n", " \n", " \n", " \n", " 0\n", - " 5.1\n", + " NaN\n", " 3.5\n", " 1.4\n", " 0.2\n", + " setosa\n", + " remove_me\n", " \n", " \n", " 1\n", @@ -363,6 +351,8 @@ " 3.0\n", " 1.4\n", " 0.2\n", + " setosa\n", + " remove_me\n", " \n", " \n", " 2\n", @@ -370,6 +360,8 @@ " 3.2\n", " 1.3\n", " 0.2\n", + " setosa\n", + " remove_me\n", " \n", " \n", " 3\n", @@ -377,6 +369,8 @@ " 3.1\n", " 1.5\n", " 0.2\n", + " setosa\n", + " remove_me\n", " \n", " \n", " ...\n", @@ -384,6 +378,8 @@ " ...\n", " ...\n", " ...\n", + " ...\n", + " ...\n", " \n", " \n", " 146\n", @@ -391,6 +387,8 @@ " 2.5\n", " 5.0\n", " 1.9\n", + " virginica\n", + " remove_me\n", " \n", " \n", " 147\n", @@ -398,6 +396,8 @@ " 3.0\n", " 5.2\n", " 2.0\n", + " virginica\n", + " remove_me\n", " \n", " \n", " 148\n", @@ -405,6 +405,8 @@ " 3.4\n", " 5.4\n", " 2.3\n", + " alieniris\n", + " remove_me\n", " \n", " \n", " 149\n", @@ -412,28 +414,30 @@ " 3.0\n", " 5.1\n", " 1.8\n", + " alieniris\n", + " remove_me\n", " \n", " \n", "\n", - "

150 rows × 4 columns

\n", + "

150 rows × 6 columns

\n", "" ], "text/plain": [ - " sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)\n", - "0 5.1 3.5 1.4 0.2\n", - "1 4.9 3.0 1.4 0.2\n", - "2 4.7 3.2 1.3 0.2\n", - "3 4.6 3.1 1.5 0.2\n", - ".. ... ... ... ...\n", - "146 6.3 2.5 5.0 1.9\n", - "147 6.5 3.0 5.2 2.0\n", - "148 6.2 3.4 5.4 2.3\n", - "149 5.9 3.0 5.1 1.8\n", + " sepallen sepalwid petallen petalwid irisname junk\n", + "0 NaN 3.5 1.4 0.2 setosa remove_me\n", + "1 4.9 3.0 1.4 0.2 setosa remove_me\n", + "2 4.7 3.2 1.3 0.2 setosa remove_me\n", + "3 4.6 3.1 1.5 0.2 setosa remove_me\n", + ".. ... ... ... ... ... ...\n", + "146 6.3 2.5 5.0 1.9 virginica remove_me\n", + "147 6.5 3.0 5.2 2.0 virginica remove_me\n", + "148 6.2 3.4 5.4 2.3 alieniris remove_me\n", + "149 5.9 3.0 5.1 1.8 alieniris remove_me\n", "\n", - "[150 rows x 4 columns]" + "[150 rows x 6 columns]" ] }, - "execution_count": 5, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -455,7 +459,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 15, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -470,15 +474,17 @@ "text": [ "\n", "RangeIndex: 150 entries, 0 to 149\n", - "Data columns (total 4 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 sepal length (cm) 150 non-null float64\n", - " 1 sepal width (cm) 150 non-null float64\n", - " 2 petal length (cm) 150 non-null float64\n", - " 3 petal width (cm) 150 non-null float64\n", - "dtypes: float64(4)\n", - "memory usage: 4.8 KB\n" + "Data columns (total 6 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 sepallen 149 non-null float64\n", + " 1 sepalwid 150 non-null float64\n", + " 2 petallen 150 non-null float64\n", + " 3 petalwid 149 non-null float64\n", + " 4 irisname 149 non-null str \n", + " 5 junk 150 non-null str \n", + "dtypes: float64(4), str(2)\n", + "memory usage: 7.2 KB\n" ] } ], @@ -495,7 +501,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 18, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -510,15 +516,16 @@ "text": [ "\n", "RangeIndex: 150 entries, 0 to 149\n", - "Data columns (total 4 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 sepal length (cm) 150 non-null float64\n", - " 1 sepal width (cm) 150 non-null float64\n", - " 2 petal length (cm) 150 non-null float64\n", - " 3 petal width (cm) 150 non-null float64\n", - "dtypes: float64(4)\n", - "memory usage: 4.8 KB\n" + "Data columns (total 5 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 sepallen 149 non-null float64\n", + " 1 sepalwid 150 non-null float64\n", + " 2 petallen 150 non-null float64\n", + " 3 petalwid 149 non-null float64\n", + " 4 irisname 149 non-null str \n", + "dtypes: float64(4), str(1)\n", + "memory usage: 6.0 KB\n" ] } ], @@ -533,13 +540,13 @@ "# if you want a list of the column names use df.columns\n", "#col5name = df.columns[5] # get column name at index 5\n", "\n", - "df_clean = df # drop by name is typical, but what else is possible?\n", + "df_clean = df.drop(columns=['junk']) # drop by name is typical, but what else is possible?\n", "df_clean.info() # Is the bad last column gone?" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 19, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -552,13 +559,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "FEATURES: Index(['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)',\n", - " 'petal width (cm)'],\n", - " dtype='str')\n", + "FEATURES: Index(['sepallen', 'sepalwid', 'petallen', 'petalwid', 'irisname'], dtype='str')\n", "\n", - "First feature: sepal length (cm)\n", + "First feature: sepallen\n", "\n", - "feature_name_to_index: {'sepal length (cm)': 0, 'sepal width (cm)': 1, 'petal length (cm)': 2, 'petal width (cm)': 3}\n" + "feature_name_to_index: {'sepallen': 0, 'sepalwid': 1, 'petallen': 2, 'petalwid': 3, 'irisname': 4}\n" ] } ], @@ -586,7 +591,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 20, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -602,50 +607,33 @@ "text": [ "\n", "RangeIndex: 150 entries, 0 to 149\n", - "Data columns (total 4 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 sepal length (cm) 150 non-null float64\n", - " 1 sepal width (cm) 150 non-null float64\n", - " 2 petal length (cm) 150 non-null float64\n", - " 3 petal width (cm) 150 non-null float64\n", - "dtypes: float64(4)\n", - "memory usage: 4.8 KB\n", + "Data columns (total 5 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 sepallen 149 non-null float64\n", + " 1 sepalwid 150 non-null float64\n", + " 2 petallen 150 non-null float64\n", + " 3 petalwid 149 non-null float64\n", + " 4 irisname 149 non-null str \n", + "dtypes: float64(4), str(1)\n", + "memory usage: 6.0 KB\n", "======================================================================\n", - " sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)\n", - "0 5.1 3.5 1.4 0.2\n", - "1 4.9 3.0 1.4 0.2\n", - "2 4.7 3.2 1.3 0.2\n", - "3 4.6 3.1 1.5 0.2\n", - ".. ... ... ... ...\n", - "146 6.3 2.5 5.0 1.9\n", - "147 6.5 3.0 5.2 2.0\n", - "148 6.2 3.4 5.4 2.3\n", - "149 5.9 3.0 5.1 1.8\n", + " sepallen sepalwid petallen petalwid irisname\n", + "0 NaN 3.5 1.4 0.2 setosa\n", + "1 4.9 3.0 1.4 0.2 setosa\n", + "2 4.7 3.2 1.3 0.2 setosa\n", + "3 4.6 3.1 1.5 0.2 setosa\n", + ".. ... ... ... ... ...\n", + "146 6.3 2.5 5.0 1.9 virginica\n", + "147 6.5 3.0 5.2 2.0 virginica\n", + "148 6.2 3.4 5.4 2.3 alieniris\n", + "149 5.9 3.0 5.1 1.8 alieniris\n", "\n", - "[150 rows x 4 columns]\n", - "======================================================================\n" - ] - }, - { - "ename": "KeyError", - "evalue": "'irisname'", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mKeyError\u001b[39m Traceback (most recent call last)", - "\u001b[36mFile \u001b[39m\u001b[32m~/.local/lib/python3.14/site-packages/pandas/core/indexes/base.py:3641\u001b[39m, in \u001b[36mIndex.get_loc\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m 3640\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m3641\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_engine\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 3642\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", - "\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/index.pyx:168\u001b[39m, in \u001b[36mpandas._libs.index.IndexEngine.get_loc\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m--> \u001b[39m\u001b[32m168\u001b[39m \u001b[33m'Could not get source, probably due dynamically evaluated source code.'\u001b[39m\n", - "\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/index.pyx:197\u001b[39m, in \u001b[36mpandas._libs.index.IndexEngine.get_loc\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m--> \u001b[39m\u001b[32m197\u001b[39m \u001b[33m'Could not get source, probably due dynamically evaluated source code.'\u001b[39m\n", - "\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/hashtable_class_helper.pxi:7668\u001b[39m, in \u001b[36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m-> \u001b[39m\u001b[32m7668\u001b[39m \u001b[33m'Could not get source, probably due dynamically evaluated source code.'\u001b[39m\n", - "\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/hashtable_class_helper.pxi:7676\u001b[39m, in \u001b[36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m-> \u001b[39m\u001b[32m7676\u001b[39m \u001b[33m'Could not get source, probably due dynamically evaluated source code.'\u001b[39m\n", - "\u001b[31mKeyError\u001b[39m: 'irisname'", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[31mKeyError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[9]\u001b[39m\u001b[32m, line 18\u001b[39m\n\u001b[32m 14\u001b[39m print(df_clean)\n\u001b[32m 15\u001b[39m print(\u001b[33m'='\u001b[39m * \u001b[32m70\u001b[39m)\n\u001b[32m 16\u001b[39m \n\u001b[32m 17\u001b[39m \u001b[38;5;66;03m# or more to the point, grab that column and inspect:\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m18\u001b[39m print(f\"Irisname entries: {df_clean[\u001b[33m'irisname'\u001b[39m].unique()}\")\n\u001b[32m 19\u001b[39m \n\u001b[32m 20\u001b[39m \u001b[38;5;66;03m# also note how the last two rows in the df have problems, among others...\u001b[39;00m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/.local/lib/python3.14/site-packages/pandas/core/frame.py:4378\u001b[39m, in \u001b[36mDataFrame.__getitem__\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m 4374\u001b[39m \n\u001b[32m 4375\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m is_single_key:\n\u001b[32m 4376\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m self.columns.nlevels > \u001b[32m1\u001b[39m:\n\u001b[32m 4377\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m self._getitem_multilevel(key)\n\u001b[32m-> \u001b[39m\u001b[32m4378\u001b[39m indexer = self.columns.get_loc(key)\n\u001b[32m 4379\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[32m 4380\u001b[39m indexer = [indexer]\n\u001b[32m 4381\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/.local/lib/python3.14/site-packages/pandas/core/indexes/base.py:3648\u001b[39m, in \u001b[36mIndex.get_loc\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m 3643\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[32m 3644\u001b[39m \u001b[38;5;28misinstance\u001b[39m(casted_key, abc.Iterable)\n\u001b[32m 3645\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[32m 3646\u001b[39m ):\n\u001b[32m 3647\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01merr\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m3648\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01merr\u001b[39;00m\n\u001b[32m 3649\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[32m 3650\u001b[39m \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[32m 3651\u001b[39m \u001b[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[32m 3652\u001b[39m \u001b[38;5;66;03m# the TypeError.\u001b[39;00m\n\u001b[32m 3653\u001b[39m \u001b[38;5;28mself\u001b[39m._check_indexing_error(key)\n", - "\u001b[31mKeyError\u001b[39m: 'irisname'" + "[150 rows x 5 columns]\n", + "======================================================================\n", + "Irisname entries: \n", + "['setosa', nan, 'versicolor', 'virginica', 'alieniris']\n", + "Length: 5, dtype: str\n" ] } ], @@ -674,7 +662,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -686,12 +674,10 @@ { "data": { "text/plain": [ - "Index(['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)',\n", - " 'petal width (cm)'],\n", - " dtype='object')" + "Index(['sepallen', 'sepalwid', 'petallen', 'petalwid', 'irisname', 'junk'], dtype='str')" ] }, - "execution_count": 19, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -702,12 +688,157 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": { "id": "xM22NWdqKPR-", "scrolled": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Index: 147 entries, 1 to 149\n", + "Data columns (total 5 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 sepallen 147 non-null float64\n", + " 1 sepalwid 147 non-null float64\n", + " 2 petallen 147 non-null float64\n", + " 3 petalwid 147 non-null float64\n", + " 4 irisname 147 non-null str \n", + "dtypes: float64(4), str(1)\n", + "memory usage: 6.9 KB\n", + "======================================================================\n" + ] + }, + { + "data": { + "text/html": [ + "
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sepallensepalwidpetallenpetalwidirisname
14.93.01.40.2setosa
24.73.21.30.2setosa
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..................
1466.32.55.01.9virginica
1476.53.05.22.0virginica
1486.23.45.42.3alieniris
1495.93.05.11.8alieniris
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147 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " sepallen sepalwid petallen petalwid irisname\n", + "1 4.9 3.0 1.4 0.2 setosa\n", + "2 4.7 3.2 1.3 0.2 setosa\n", + "3 4.6 3.1 1.5 0.2 setosa\n", + "4 5.0 3.6 1.4 0.2 setosa\n", + ".. ... ... ... ... ...\n", + "146 6.3 2.5 5.0 1.9 virginica\n", + "147 6.5 3.0 5.2 2.0 virginica\n", + "148 6.2 3.4 5.4 2.3 alieniris\n", + "149 5.9 3.0 5.1 1.8 alieniris\n", + "\n", + "[147 rows x 5 columns]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "##############################\n", "# BLOCK 9: USING DF'S dropna\n", @@ -730,11 +861,167 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": { "id": "h0spkxGzKPR-" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Irisname entries: \n", + "['setosa', 'versicolor', 'virginica', 'alieniris']\n", + "Length: 4, dtype: str\n", + "1 False\n", + "2 False\n", + "3 False\n", + "4 False\n", + " ... \n", + "146 False\n", + "147 False\n", + "148 True\n", + "149 True\n", + "Name: irisname, Length: 147, dtype: bool\n", + "1 True\n", + "2 True\n", + "3 True\n", + "4 True\n", + " ... \n", + "146 True\n", + "147 True\n", + "148 False\n", + "149 False\n", + "Name: irisname, Length: 147, dtype: bool\n", + "(145, 5)\n" + ] + }, + { + "data": { + "text/html": [ + "
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sepallensepalwidpetallenpetalwidirisname
14.93.01.40.2setosa
24.73.21.30.2setosa
34.63.11.50.2setosa
45.03.61.40.2setosa
..................
1446.73.35.72.5virginica
1456.73.05.22.3virginica
1466.32.55.01.9virginica
1476.53.05.22.0virginica
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145 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " sepallen sepalwid petallen petalwid irisname\n", + "1 4.9 3.0 1.4 0.2 setosa\n", + "2 4.7 3.2 1.3 0.2 setosa\n", + "3 4.6 3.1 1.5 0.2 setosa\n", + "4 5.0 3.6 1.4 0.2 setosa\n", + ".. ... ... ... ... ...\n", + "144 6.7 3.3 5.7 2.5 virginica\n", + "145 6.7 3.0 5.2 2.3 virginica\n", + "146 6.3 2.5 5.0 1.9 virginica\n", + "147 6.5 3.0 5.2 2.0 virginica\n", + "\n", + "[145 rows x 5 columns]" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "################################\n", "# BLOCK 10: REMOVING BOGUS DATA\n", @@ -748,7 +1035,7 @@ "# define a final version of the DataFrame by pulling out the\n", "# bad alieniris data (remember that you can pass a boolean\n", "# Series to select data that you want)\n", - "df_final = pass\n", + "df_final = df_clean[df_clean['irisname'] != 'alieniris'].copy()\n", "# ^^^^ YOU NEED TO WRITE CODE HERE...\n", "\n", "print(df_final.shape)\n", @@ -757,11 +1044,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": { "id": "EywtvRyMKPR-" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'setosa': 0, 'versicolor': 1, 'virginica': 2}\n", + "\n", + "setosa maps to 0\n", + "versicolor maps to 1\n", + "virginica maps to 2\n" + ] + } + ], "source": [ "##########################################\n", "# BLOCK 11: CONVERT SPECIES NAME TO INDEX\n", @@ -788,11 +1087,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": { "id": "NuKqemiiKPR_" }, - "outputs": [], + "outputs": [ + { + "ename": "KeyError", + "evalue": "0", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mKeyError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[28]\u001b[39m\u001b[32m, line 14\u001b[39m\n\u001b[32m 10\u001b[39m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[32m 11\u001b[39m \u001b[38;5;66;03m# >>> ADD CODE HERE TO KEEP THE WARNING FROM HAPPENING <<<\u001b[39;00m\n\u001b[32m 12\u001b[39m \n\u001b[32m 13\u001b[39m df_final[\u001b[33m'irisname'\u001b[39m] = df_final[\u001b[33m'irisname'\u001b[39m].apply(convertSpecies)\n\u001b[32m---> \u001b[39m\u001b[32m14\u001b[39m df_final.loc[:, \u001b[33m'irisname'\u001b[39m] = df_final[\u001b[33m'irisname'\u001b[39m].apply(convertSpecies)\n\u001b[32m 15\u001b[39m \u001b[38;5;66;03m# Don't run this twice! Why?! What's \"KeyError: 0\"?\u001b[39;00m\n\u001b[32m 16\u001b[39m \u001b[38;5;66;03m# (of course, you can always go back and re-establish definitions of df_final)\u001b[39;00m\n\u001b[32m 17\u001b[39m \n", + "\u001b[36mFile \u001b[39m\u001b[32m~/.local/lib/python3.14/site-packages/pandas/core/series.py:5084\u001b[39m, in \u001b[36mSeries.apply\u001b[39m\u001b[34m(self, func, args, by_row, **kwargs)\u001b[39m\n\u001b[32m 4960\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mapply\u001b[39m(\n\u001b[32m 4961\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m 4962\u001b[39m func: AggFuncType,\n\u001b[32m (...)\u001b[39m\u001b[32m 4966\u001b[39m **kwargs,\n\u001b[32m 4967\u001b[39m ) -> DataFrame | Series:\n\u001b[32m 4968\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 4969\u001b[39m \u001b[33;03m Invoke function on values of Series.\u001b[39;00m\n\u001b[32m 4970\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 5076\u001b[39m \u001b[33;03m dtype: float64\u001b[39;00m\n\u001b[32m 5077\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m 5078\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mSeriesApply\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 5079\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 5080\u001b[39m \u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 5081\u001b[39m \u001b[43m \u001b[49m\u001b[43mby_row\u001b[49m\u001b[43m=\u001b[49m\u001b[43mby_row\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 5082\u001b[39m \u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m=\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 5083\u001b[39m \u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m=\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m-> \u001b[39m\u001b[32m5084\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/.local/lib/python3.14/site-packages/pandas/core/apply.py:1520\u001b[39m, in \u001b[36mSeriesApply.apply\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 1517\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.apply_compat()\n\u001b[32m 1519\u001b[39m \u001b[38;5;66;03m# self.func is Callable\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1520\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mapply_standard\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/.local/lib/python3.14/site-packages/pandas/core/apply.py:1578\u001b[39m, in \u001b[36mSeriesApply.apply_standard\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 1576\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 1577\u001b[39m curried = func\n\u001b[32m-> \u001b[39m\u001b[32m1578\u001b[39m mapped = \u001b[43mobj\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_map_values\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmapper\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcurried\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1580\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(mapped) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(mapped[\u001b[32m0\u001b[39m], ABCSeries):\n\u001b[32m 1581\u001b[39m \u001b[38;5;66;03m# GH#43986 Need to do list(mapped) in order to get treated as nested\u001b[39;00m\n\u001b[32m 1582\u001b[39m \u001b[38;5;66;03m# See also GH#25959 regarding EA support\u001b[39;00m\n\u001b[32m 1583\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m obj._constructor_expanddim(\u001b[38;5;28mlist\u001b[39m(mapped), index=obj.index)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/.local/lib/python3.14/site-packages/pandas/core/base.py:1022\u001b[39m, in \u001b[36mIndexOpsMixin._map_values\u001b[39m\u001b[34m(self, mapper, na_action)\u001b[39m\n\u001b[32m 1019\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(arr, ExtensionArray):\n\u001b[32m 1020\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m arr.map(mapper, na_action=na_action)\n\u001b[32m-> \u001b[39m\u001b[32m1022\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43malgorithms\u001b[49m\u001b[43m.\u001b[49m\u001b[43mmap_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43marr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmapper\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mna_action\u001b[49m\u001b[43m=\u001b[49m\u001b[43mna_action\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/.local/lib/python3.14/site-packages/pandas/core/algorithms.py:1710\u001b[39m, in \u001b[36mmap_array\u001b[39m\u001b[34m(arr, mapper, na_action)\u001b[39m\n\u001b[32m 1708\u001b[39m values = arr.astype(\u001b[38;5;28mobject\u001b[39m, copy=\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[32m 1709\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m na_action \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1710\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mlib\u001b[49m\u001b[43m.\u001b[49m\u001b[43mmap_infer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmapper\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1711\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 1712\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m lib.map_infer_mask(values, mapper, mask=isna(values).view(np.uint8))\n", + "\u001b[36mFile \u001b[39m\u001b[32mpandas/_libs/lib.pyx:3071\u001b[39m, in \u001b[36mpandas._libs.lib.map_infer\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m-> \u001b[39m\u001b[32m3071\u001b[39m \u001b[33m'Could not get source, probably due dynamically evaluated source code.'\u001b[39m\n", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[24]\u001b[39m\u001b[32m, line 17\u001b[39m, in \u001b[36mconvertSpecies\u001b[39m\u001b[34m(species_name)\u001b[39m\n\u001b[32m 14\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m convertSpecies(species_name: str) -> int:\n\u001b[32m 15\u001b[39m \u001b[33m''' return the species index (a unique integer/category) '''\u001b[39m\n\u001b[32m 16\u001b[39m \u001b[38;5;66;03m#print(f\"converting {species_name}...\")\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m17\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m species_name_to_index[species_name]\n", + "\u001b[31mKeyError\u001b[39m: 0" + ] + } + ], "source": [ "##########################################\n", "# BLOCK 12: USING DF'S .apply\n", @@ -805,8 +1123,9 @@ "# see https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "#\n", "# >>> ADD CODE HERE TO KEEP THE WARNING FROM HAPPENING <<<\n", - "df_final['irisname'] = df_final['irisname'].apply(convertSpecies)\n", "\n", + "df_final['irisname'] = df_final['irisname'].apply(convertSpecies)\n", + "df_final.loc[:, 'irisname'] = df_final['irisname'].apply(convertSpecies)\n", "# Don't run this twice! Why?! What's \"KeyError: 0\"?\n", "# (of course, you can always go back and re-establish definitions of df_final)\n", "\n" @@ -814,11 +1133,137 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": { "id": "tWoVKJBGKPR_" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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sepallensepalwidpetallenpetalwidirisname
14.93.01.40.20
24.73.21.30.20
34.63.11.50.20
45.03.61.40.20
..................
1446.73.35.72.52
1456.73.05.22.32
1466.32.55.01.92
1476.53.05.22.02
\n", + "

145 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " sepallen sepalwid petallen petalwid irisname\n", + "1 4.9 3.0 1.4 0.2 0\n", + "2 4.7 3.2 1.3 0.2 0\n", + "3 4.6 3.1 1.5 0.2 0\n", + "4 5.0 3.6 1.4 0.2 0\n", + ".. ... ... ... ... ...\n", + "144 6.7 3.3 5.7 2.5 2\n", + "145 6.7 3.0 5.2 2.3 2\n", + "146 6.3 2.5 5.0 1.9 2\n", + "147 6.5 3.0 5.2 2.0 2\n", + "\n", + "[145 rows x 5 columns]" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "##########################################\n", "# BLOCK 13: CONFIRMING FINAL DATAFRAME\n", @@ -832,11 +1277,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": { "id": "qxpBs7NnKPR_" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "type of A: \n", + " sepallen sepalwid petallen petalwid irisname\n", + "1 4.9 3.0 1.4 0.2 0\n", + "2 4.7 3.2 1.3 0.2 0\n", + "3 4.6 3.1 1.5 0.2 0\n", + "4 5.0 3.6 1.4 0.2 0\n", + "5 5.4 3.9 1.7 0.4 0\n", + "A[0] = [4.9 3. 1.4 0.2 0. ]\n" + ] + } + ], "source": [ "##########################################\n", "# BLOCK 14: CONVERTING TO NUMPY FORMAT\n", @@ -856,11 +1316,166 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": { "id": "bblbBuIiKPSA" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([[4.9, 3. , 1.4, 0.2, 0. ],\n", + " [4.7, 3.2, 1.3, 0.2, 0. ],\n", + " [4.6, 3.1, 1.5, 0.2, 0. ],\n", + " [5. , 3.6, 1.4, 0.2, 0. ],\n", + " [5.4, 3.9, 1.7, 0.4, 0. ],\n", + " [4.6, 3.4, 1.4, 0.3, 0. ],\n", + " [4.4, 2.9, 1.4, 0.2, 0. ],\n", + " [4.9, 3.1, 1.5, 0.1, 0. ],\n", + " [5.4, 3.7, 1.5, 0.2, 0. ],\n", + " [4.8, 3.4, 1.6, 0.2, 0. ],\n", + " [4.8, 3. , 1.4, 0.1, 0. ],\n", + " [4.3, 3. , 1.1, 0.1, 0. ],\n", + " [5.8, 4. , 1.2, 0.2, 0. ],\n", + " [5.7, 4.4, 1.5, 0.4, 0. ],\n", + " [5.4, 3.9, 1.3, 0.4, 0. ],\n", + " [5.1, 3.5, 1.4, 0.3, 0. ],\n", + " [5.7, 3.8, 1.7, 0.3, 0. ],\n", + " [5.1, 3.8, 1.5, 0.3, 0. ],\n", + 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[6.7 3.1 5.6 2.4 2. ]\n", + " [6.9 3.1 5.1 2.3 2. ]\n", + " [5.8 2.7 5.1 1.9 2. ]\n", + " [6.8 3.2 5.9 2.3 2. ]\n", + " [6.7 3.3 5.7 2.5 2. ]\n", + " [6.7 3. 5.2 2.3 2. ]\n", + " [6.3 2.5 5. 1.9 2. ]\n", + " [6.5 3. 5.2 2. 2. ]]\n" + ] + } + ], "source": [ "##########################################\n", "# BLOCK 16: USING NUMPY'S .shape\n", @@ -895,12 +1664,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": { "id": "ixiZhsuqKPSA", "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "flower #132 data is [7.7 3. 6.1 2.3 2. ]\n", + " Its sepallen is 7.7\n", + " Its sepalwid is 3.0\n", + " Its petallen is 6.1\n", + " Its petalwid is 2.3\n", + " Its irisname is virginica (2)\n" + ] + } + ], "source": [ "##########################################\n", "# BLOCK 17: PRINTING FLOWER INFO\n", @@ -924,11 +1706,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": { "id": "izGld2tSKPSA" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I predict versicolor (1) from features [4.6, 3.6, 3.0, 1.2]\n" + ] + } + ], "source": [ "##########################################\n", "# BLOCK 18: WRITING OUR OWN 1-NN FUNCTION\n", @@ -985,7 +1775,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": { "id": "9nhDlGbRKPSB" }, @@ -1006,11 +1796,174 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": { "id": "64kSAiCvKPSB" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+++ Start of data definitions +++\n", + "\n", + "X_all (just features) is \n", + " [[4.9 3. 1.4 0.2]\n", + " [4.7 3.2 1.3 0.2]\n", + " [4.6 3.1 1.5 0.2]\n", + " [5. 3.6 1.4 0.2]\n", + " [5.4 3.9 1.7 0.4]\n", + " [4.6 3.4 1.4 0.3]\n", + " [4.4 2.9 1.4 0.2]\n", + " [4.9 3.1 1.5 0.1]\n", + " [5.4 3.7 1.5 0.2]\n", + " [4.8 3.4 1.6 0.2]\n", + " [4.8 3. 1.4 0.1]\n", + " [4.3 3. 1.1 0.1]\n", + " [5.8 4. 1.2 0.2]\n", + " [5.7 4.4 1.5 0.4]\n", + " [5.4 3.9 1.3 0.4]\n", + " [5.1 3.5 1.4 0.3]\n", + " [5.7 3.8 1.7 0.3]\n", + " [5.1 3.8 1.5 0.3]\n", + " [5.4 3.4 1.7 0.2]\n", + " [5.1 3.7 1.5 0.4]\n", + " [4.6 3.6 1. 0.2]\n", + " [5.1 3.3 1.7 0.5]\n", + " [4.8 3.4 1.9 0.2]\n", + " [5. 3.4 1.6 0.4]\n", + " [5.2 3.5 1.5 0.2]\n", + " [5.2 3.4 1.4 0.2]\n", + " [4.7 3.2 1.6 0.2]\n", + " [4.8 3.1 1.6 0.2]\n", + " [5.4 3.4 1.5 0.4]\n", + " [5.2 4.1 1.5 0.1]\n", + " [5.5 4.2 1.4 0.2]\n", + " [4.9 3.1 1.5 0.2]\n", + " [5. 3.2 1.2 0.2]\n", + " [5.5 3.5 1.3 0.2]\n", + " [4.9 3.6 1.4 0.1]\n", + " [4.4 3. 1.3 0.2]\n", + " [5.1 3.4 1.5 0.2]\n", + " [5. 3.5 1.3 0.3]\n", + " [4.5 2.3 1.3 0.3]\n", + " [4.4 3.2 1.3 0.2]\n", + " [5. 3.5 1.6 0.6]\n", + " [5.1 3.8 1.9 0.4]\n", + " [4.8 3. 1.4 0.3]\n", + " [5.1 3.8 1.6 0.2]\n", + " [4.6 3.2 1.4 0.2]\n", + " [5.3 3.7 1.5 0.2]\n", + " [5. 3.3 1.4 0.2]\n", + " [7. 3.2 4.7 1.4]\n", + " [6.4 3.2 4.5 1.5]\n", + " [6.9 3.1 4.9 1.5]\n", + " [5.5 2.3 4. 1.3]\n", + " [6.5 2.8 4.6 1.5]\n", + " [5.7 2.8 4.5 1.3]\n", + " [6.3 3.3 4.7 1.6]\n", + " [4.9 2.4 3.3 1. ]\n", + " [6.6 2.9 4.6 1.3]\n", + " [5.2 2.7 3.9 1.4]\n", + " [5. 2. 3.5 1. ]\n", + " [5.9 3. 4.2 1.5]\n", + " [6. 2.2 4. 1. ]\n", + " [6.1 2.9 4.7 1.4]\n", + " [5.6 2.9 3.6 1.3]\n", + " [6.7 3.1 4.4 1.4]\n", + " [5.6 3. 4.5 1.5]\n", + " [5.8 2.7 4.1 1. ]\n", + " [6.2 2.2 4.5 1.5]\n", + " [5.6 2.5 3.9 1.1]\n", + " [5.9 3.2 4.8 1.8]\n", + " [6.1 2.8 4. 1.3]\n", + " [6.3 2.5 4.9 1.5]\n", + " [6.1 2.8 4.7 1.2]\n", + " [6.4 2.9 4.3 1.3]\n", + " [6.6 3. 4.4 1.4]\n", + " [6.8 2.8 4.8 1.4]\n", + " [6.7 3. 5. 1.7]\n", + " [6. 2.9 4.5 1.5]\n", + " [5.7 2.6 3.5 1. ]\n", + " [5.5 2.4 3.8 1.1]\n", + " [5.5 2.4 3.7 1. ]\n", + " [5.8 2.7 3.9 1.2]\n", + " [6. 2.7 5.1 1.6]\n", + " [5.4 3. 4.5 1.5]\n", + " [6. 3.4 4.5 1.6]\n", + " [6.7 3.1 4.7 1.5]\n", + " [6.3 2.3 4.4 1.3]\n", + " [5.6 3. 4.1 1.3]\n", + " [5.5 2.5 4. 1.3]\n", + " [5.5 2.6 4.4 1.2]\n", + " [6.1 3. 4.6 1.4]\n", + " [5.8 2.6 4. 1.2]\n", + " [5. 2.3 3.3 1. ]\n", + " [5.6 2.7 4.2 1.3]\n", + " [5.7 3. 4.2 1.2]\n", + " [5.7 2.9 4.2 1.3]\n", + " [6.2 2.9 4.3 1.3]\n", + " [5.1 2.5 3. 1.1]\n", + " [5.7 2.8 4.1 1.3]\n", + " [6.3 3.3 6. 2.5]\n", + " [5.8 2.7 5.1 1.9]\n", + " [7.1 3. 5.9 2.1]\n", + " [6.3 2.9 5.6 1.8]\n", + " [6.5 3. 5.8 2.2]\n", + " [7.6 3. 6.6 2.1]\n", + " [4.9 2.5 4.5 1.7]\n", + " [7.3 2.9 6.3 1.8]\n", + " [6.7 2.5 5.8 1.8]\n", + " [7.2 3.6 6.1 2.5]\n", + " [6.5 3.2 5.1 2. ]\n", + " [6.4 2.7 5.3 1.9]\n", + " [6.8 3. 5.5 2.1]\n", + " [5.7 2.5 5. 2. ]\n", + " [5.8 2.8 5.1 2.4]\n", + " [6.4 3.2 5.3 2.3]\n", + " [6.5 3. 5.5 1.8]\n", + " [7.7 3.8 6.7 2.2]\n", + " [7.7 2.6 6.9 2.3]\n", + " [6. 2.2 5. 1.5]\n", + " [6.9 3.2 5.7 2.3]\n", + " [5.6 2.8 4.9 2. ]\n", + " [7.7 2.8 6.7 2. ]\n", + " [6.3 2.7 4.9 1.8]\n", + " [6.7 3.3 5.7 2.1]\n", + " [7.2 3.2 6. 1.8]\n", + " [6.2 2.8 4.8 1.8]\n", + " [6.1 3. 4.9 1.8]\n", + " [6.4 2.8 5.6 2.1]\n", + " [7.2 3. 5.8 1.6]\n", + " [7.4 2.8 6.1 1.9]\n", + " [7.9 3.8 6.4 2. ]\n", + " [6.4 2.8 5.6 2.2]\n", + " [6.3 2.8 5.1 1.5]\n", + " [6.1 2.6 5.6 1.4]\n", + " [7.7 3. 6.1 2.3]\n", + " [6.3 3.4 5.6 2.4]\n", + " [6.4 3.1 5.5 1.8]\n", + " [6. 3. 4.8 1.8]\n", + " [6.9 3.1 5.4 2.1]\n", + " [6.7 3.1 5.6 2.4]\n", + " [6.9 3.1 5.1 2.3]\n", + " [5.8 2.7 5.1 1.9]\n", + " [6.8 3.2 5.9 2.3]\n", + " [6.7 3.3 5.7 2.5]\n", + " [6.7 3. 5.2 2.3]\n", + " [6.3 2.5 5. 1.9]\n", + " [6.5 3. 5.2 2. ]]\n", + "y_all (just labels) is \n", + " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.\n", + " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n", + " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n", + " 1. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2.\n", + " 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2.\n", + " 2.]\n" + ] + } + ], "source": [ "################################################\n", "# BLOCK 20: DEFINIING FEATURES & LABELS FOR kNN\n", @@ -1027,11 +1980,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": { "id": "2ChEk2CDKPSC" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Weighting sepallen by 1.0\n", + "Weighting sepalwid by 1.0\n", + "Weighting petallen by 1.0\n", + "Weighting petalwid by 1.0\n" + ] + } + ], "source": [ "################################################\n", "# BLOCK 21: REWEIGHTING FEATURES\n", @@ -1058,11 +2022,170 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": { "id": "sTDi2mFnKPSC" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[7.7 2.6 6.9 2.3]\n", + " [4.3 3. 1.1 0.1]\n", + " [5.4 3.7 1.5 0.2]\n", + " [7.2 3.6 6.1 2.5]\n", + " [6.3 3.3 6. 2.5]\n", + " [5.7 2.6 3.5 1. ]\n", + " [4.8 3.4 1.9 0.2]\n", + " [6.7 3.1 4.7 1.5]\n", + " [6.1 3. 4.9 1.8]\n", + " [6.9 3.1 5.4 2.1]\n", + " [5.4 3.4 1.7 0.2]\n", + " [7.3 2.9 6.3 1.8]\n", + " [6.6 2.9 4.6 1.3]\n", + " [6.3 2.5 5. 1.9]\n", + " [5.4 3.9 1.7 0.4]\n", + " [6.4 3.2 5.3 2.3]\n", + " [5.5 2.4 3.7 1. ]\n", + " [5.4 3.4 1.5 0.4]\n", + " [5.8 2.8 5.1 2.4]\n", + " [4.6 3.4 1.4 0.3]\n", + " [7.2 3. 5.8 1.6]\n", + " [4.6 3.2 1.4 0.2]\n", + " [6.9 3.1 4.9 1.5]\n", + " [5.1 3.3 1.7 0.5]\n", + " [5.3 3.7 1.5 0.2]\n", + " [5. 3.5 1.6 0.6]\n", + " [5.6 2.7 4.2 1.3]\n", + " [6.4 2.7 5.3 1.9]\n", + " [5.9 3.2 4.8 1.8]\n", + " [6.7 3.1 4.4 1.4]\n", + " [4.8 3. 1.4 0.1]\n", + " [6.3 2.7 4.9 1.8]\n", + " [5. 3.3 1.4 0.2]\n", + " [7.4 2.8 6.1 1.9]\n", + " [4.9 3.1 1.5 0.2]\n", + " [5.1 3.8 1.9 0.4]\n", + " [6.9 3.1 5.1 2.3]\n", + " [5.1 3.5 1.4 0.3]\n", + " [6.1 2.8 4.7 1.2]\n", + " [6.8 2.8 4.8 1.4]\n", + " [4.7 3.2 1.6 0.2]\n", + " [4.9 3.6 1.4 0.1]\n", + " [5.2 4.1 1.5 0.1]\n", + " [5.5 3.5 1.3 0.2]\n", + " [4.4 3. 1.3 0.2]\n", + " [5.2 3.5 1.5 0.2]\n", + " [5.7 3. 4.2 1.2]\n", + " [4.4 3.2 1.3 0.2]\n", + " [5.2 3.4 1.4 0.2]\n", + " [5.8 2.7 5.1 1.9]\n", + " [6.1 2.6 5.6 1.4]\n", + " [5.1 2.5 3. 1.1]\n", + " [4.9 2.5 4.5 1.7]\n", + " [6.5 3.2 5.1 2. ]\n", + " [5. 3.4 1.6 0.4]\n", + " [6.7 3.1 5.6 2.4]\n", + " [5. 3.6 1.4 0.2]\n", + " [5.7 2.9 4.2 1.3]\n", + " [5.4 3.9 1.3 0.4]\n", + " [5. 3.2 1.2 0.2]\n", + " [4.9 3. 1.4 0.2]\n", + " [5.7 2.8 4.1 1.3]\n", + " [6.8 3.2 5.9 2.3]\n", + " [7.9 3.8 6.4 2. ]\n", + " [6. 3.4 4.5 1.6]\n", + " [6.1 2.8 4. 1.3]\n", + " [5.5 2.4 3.8 1.1]\n", + " [6.3 2.5 4.9 1.5]\n", + " [5.7 2.8 4.5 1.3]\n", + " [6.7 3.3 5.7 2.1]\n", + " [6.3 2.3 4.4 1.3]\n", + " [4.7 3.2 1.3 0.2]\n", + " [6. 2.9 4.5 1.5]\n", + " [6. 2.7 5.1 1.6]\n", + " [4.8 3.4 1.6 0.2]\n", + " [5. 2. 3.5 1. ]\n", + " [4.5 2.3 1.3 0.3]\n", + " [4.6 3.1 1.5 0.2]\n", + " [6.7 3. 5.2 2.3]\n", + " [6.6 3. 4.4 1.4]\n", + " [6.8 3. 5.5 2.1]\n", + " [6.9 3.2 5.7 2.3]\n", + " [5.6 3. 4.5 1.5]\n", + " [6.7 3. 5. 1.7]\n", + " [6.7 2.5 5.8 1.8]\n", + " [6.5 2.8 4.6 1.5]\n", + " [5.8 2.6 4. 1.2]\n", + " [4.6 3.6 1. 0.2]\n", + " [5.1 3.8 1.5 0.3]\n", + " [5.5 4.2 1.4 0.2]\n", + " [6.4 3.2 4.5 1.5]\n", + " [5.7 4.4 1.5 0.4]\n", + " [4.8 3. 1.4 0.3]\n", + " [7.1 3. 5.9 2.1]\n", + " [6.4 3.1 5.5 1.8]\n", + " [6.1 2.9 4.7 1.4]\n", + " [6.5 3. 5.8 2.2]\n", + " [5.9 3. 4.2 1.5]\n", + " [4.9 3.1 1.5 0.1]\n", + " [7.6 3. 6.6 2.1]\n", + " [7.7 3.8 6.7 2.2]\n", + " [6.2 2.9 4.3 1.3]\n", + " [4.8 3.1 1.6 0.2]\n", + " [6. 2.2 4. 1. ]\n", + " [6.5 3. 5.5 1.8]\n", + " [6.1 3. 4.6 1.4]\n", + " [5.6 2.9 3.6 1.3]\n", + " [5.1 3.4 1.5 0.2]\n", + " [6.3 3.4 5.6 2.4]\n", + " [6.3 2.9 5.6 1.8]\n", + " [6.5 3. 5.2 2. ]\n", + " [5.8 4. 1.2 0.2]\n", + " [4.4 2.9 1.4 0.2]\n", + " [6.2 2.2 4.5 1.5]\n", + " [5.8 2.7 5.1 1.9]\n", + " [5.8 2.7 4.1 1. ]\n", + " [6. 3. 4.8 1.8]\n", + " [6. 2.2 5. 1.5]\n", + " [6.3 2.8 5.1 1.5]\n", + " [6.4 2.8 5.6 2.2]\n", + " [5.5 2.6 4.4 1.2]\n", + " [7.7 3. 6.1 2.3]\n", + " [5.6 2.8 4.9 2. ]\n", + " [5. 2.3 3.3 1. ]\n", + " [7.7 2.8 6.7 2. ]\n", + " [5.5 2.3 4. 1.3]\n", + " [5. 3.5 1.3 0.3]\n", + " [7. 3.2 4.7 1.4]\n", + " [4.9 2.4 3.3 1. ]\n", + " [5.1 3.7 1.5 0.4]\n", + " [5.5 2.5 4. 1.3]\n", + " [5.8 2.7 3.9 1.2]\n", + " [5.7 3.8 1.7 0.3]\n", + " [6.4 2.8 5.6 2.1]\n", + " [7.2 3.2 6. 1.8]\n", + " [6.3 3.3 4.7 1.6]\n", + " [6.4 2.9 4.3 1.3]\n", + " [5.6 2.5 3.9 1.1]\n", + " [6.2 2.8 4.8 1.8]\n", + " [5.4 3. 4.5 1.5]\n", + " [6.7 3.3 5.7 2.5]\n", + " [5.1 3.8 1.6 0.2]\n", + " [5.7 2.5 5. 2. ]\n", + " [5.2 2.7 3.9 1.4]\n", + " [5.6 3. 4.1 1.3]]\n", + "[2. 0. 0. 2. 2. 1. 0. 1. 2. 2. 0. 2. 1. 2. 0. 2. 1. 0. 2. 0. 2. 0. 1. 0.\n", + " 0. 0. 1. 2. 1. 1. 0. 2. 0. 2. 0. 0. 2. 0. 1. 1. 0. 0. 0. 0. 0. 0. 1. 0.\n", + " 0. 2. 2. 1. 2. 2. 0. 2. 0. 1. 0. 0. 0. 1. 2. 2. 1. 1. 1. 1. 1. 2. 1. 0.\n", + " 1. 1. 0. 1. 0. 0. 2. 1. 2. 2. 1. 1. 2. 1. 1. 0. 0. 0. 1. 0. 0. 2. 2. 1.\n", + " 2. 1. 0. 2. 2. 1. 0. 1. 2. 1. 1. 0. 2. 2. 2. 0. 0. 1. 2. 1. 2. 2. 2. 2.\n", + " 1. 2. 2. 1. 2. 1. 0. 1. 1. 0. 1. 1. 0. 2. 2. 1. 1. 1. 2. 1. 2. 0. 2. 1.\n", + " 1.]\n" + ] + } + ], "source": [ "################################################\n", "# BLOCK 22: PERMUTING THE DATA\n", @@ -1083,11 +2206,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": { "id": "_pD7IZR1KPSC" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total rows: 145; training with 116 rows; testing with 29 rows\n", + "\t(sanity check: 116 + 29 = 145)\n" + ] + } + ], "source": [ "################################################\n", "# BLOCK 23: DEFINING TRAIN VS TEST SETS\n", @@ -1122,11 +2254,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "metadata": { "id": "jvJMYC3eKPSD" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Created and trained a knn classifier with k = 84\n" + ] + } + ], "source": [ "#######################################################\n", "# BLOCK 24: FIRST ATTEMPT TO BUILD & TRAIN A kNN MODEL\n", @@ -1149,11 +2289,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "metadata": { "id": "d3cB0xryKPSD" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicted labels: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n", + " 1. 1. 1. 1. 1.]\n", + "Actual labels : [2. 0. 0. 2. 2. 1. 0. 1. 2. 2. 0. 2. 1. 2. 0. 2. 1. 0. 2. 0. 2. 0. 1. 0.\n", + " 0. 0. 1. 2. 1.]\n", + "\n", + "Results on test set: 7 correct out of 29 total.\n" + ] + } + ], "source": [ "################################################\n", "# BLOCK 25: TEST THE kNN MODEL\n", @@ -1181,11 +2334,59 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "metadata": { "id": "liXBT5-IKPSD" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "row 0 : versicolor virginica incorrect\n", + "row 1 : versicolor setosa incorrect\n", + "row 2 : versicolor setosa incorrect\n", + "row 3 : versicolor virginica incorrect\n", + "row 4 : versicolor virginica incorrect\n", + "row 5 : versicolor versicolor \n", + "row 6 : versicolor setosa incorrect\n", + "row 7 : versicolor versicolor \n", + "row 8 : versicolor virginica incorrect\n", + "row 9 : versicolor virginica incorrect\n", + "row 10 : versicolor setosa incorrect\n", + "row 11 : versicolor virginica incorrect\n", + "row 12 : versicolor versicolor \n", + "row 13 : versicolor virginica incorrect\n", + "row 14 : versicolor setosa incorrect\n", + "row 15 : versicolor virginica incorrect\n", + "row 16 : versicolor versicolor \n", + "row 17 : versicolor setosa incorrect\n", + "row 18 : versicolor virginica incorrect\n", + "row 19 : versicolor setosa incorrect\n", + "row 20 : versicolor virginica incorrect\n", + "row 21 : versicolor setosa incorrect\n", + "row 22 : versicolor versicolor \n", + "row 23 : versicolor setosa incorrect\n", + "row 24 : versicolor setosa incorrect\n", + "row 25 : versicolor setosa incorrect\n", + "row 26 : versicolor versicolor \n", + "row 27 : versicolor virginica incorrect\n", + "row 28 : versicolor versicolor \n", + "\n", + "Correct: 7 out of 29\n" + ] + }, + { + "data": { + "text/plain": [ + "7" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "################################################\n", "# BLOCK 26: PRETTY-PRINT PREDICTED LABELS\n", @@ -1228,11 +2429,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "metadata": { "id": "h_ZigXq2KPSE" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "predicted_species = [1.]\n", + "I predict versicolor (1) from features [6.7, 3.3, 5.7, 2.1]\n" + ] + } + ], "source": [ "################################################\n", "# BLOCK 27: USE THE FIRST-ATTEMPT kNN MODEL\n", @@ -1272,7 +2482,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "metadata": { "id": "kPByHBBnKPSE" }, @@ -1296,11 +2506,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "metadata": { "id": "ZUmotr7hKPSE" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "k: 1 cv accuracy: 0.9482\n" + ] + }, + { + "ename": "NameError", + "evalue": "name 'best_accuracy' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mNameError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[45]\u001b[39m\u001b[32m, line 28\u001b[39m\n\u001b[32m 24\u001b[39m cv_scores = cross_val_score(knn_cv_model, X_train, y_train, cv=\u001b[32m5\u001b[39m)\n\u001b[32m 25\u001b[39m this_cv_accuracy = cv_scores.mean()\n\u001b[32m 26\u001b[39m print(f\"k: {k:2d} cv accuracy: {this_cv_accuracy:7.4f}\")\n\u001b[32m 27\u001b[39m all_accuracies.append(this_cv_accuracy)\n\u001b[32m---> \u001b[39m\u001b[32m28\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m this_cv_accuracy > best_accuracy:\n\u001b[32m 29\u001b[39m best_accuracy = this_cv_accuracy\n\u001b[32m 30\u001b[39m best_k = k\n\u001b[32m 31\u001b[39m \u001b[38;5;66;03m# assign best value of k to best_k\u001b[39;00m\n", + "\u001b[31mNameError\u001b[39m: name 'best_accuracy' is not defined" + ] + } + ], "source": [ "################################################\n", "# BLOCK 29: USING CROSS VALIDATION\n", @@ -1324,12 +2553,14 @@ "all_accuracies = []\n", "\n", "for k in range(1, max_k, 1):\n", - " knn_cv_model = KNeighborsClassifier(n_neighbors = k) # build knn_model for every k!\n", - " cv_scores = cross_val_score( knn_cv_model, X_train, y_train, cv = 5 ) # 5 means 80/20 split\n", - " this_cv_accuracy = cv_scores.mean() # mean() is numpy's built-in average function\n", + " knn_cv_model = KNeighborsClassifier(n_neighbors=k)\n", + " cv_scores = cross_val_score(knn_cv_model, X_train, y_train, cv=5)\n", + " this_cv_accuracy = cv_scores.mean()\n", " print(f\"k: {k:2d} cv accuracy: {this_cv_accuracy:7.4f}\")\n", " all_accuracies.append(this_cv_accuracy)\n", - "\n", + " if this_cv_accuracy > best_accuracy:\n", + " best_accuracy = this_cv_accuracy\n", + " best_k = k\n", "# assign best value of k to best_k\n", "best_k = k # *** AT THE MOMENT THIS IS INCORRECT ***\n", "# you'll need to modify the loop above to find and remember the real best_k\n", @@ -1346,11 +2577,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "metadata": { "id": "pJW6c8XdKPSF" }, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'best_k' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mNameError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[46]\u001b[39m\u001b[32m, line 11\u001b[39m\n\u001b[32m 7\u001b[39m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[32m 8\u001b[39m \u001b[38;5;66;03m# Now, using best_k instead of the original, randomly-guessed value How does it do?!\u001b[39;00m\n\u001b[32m 9\u001b[39m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[32m 10\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m sklearn.neighbors \u001b[38;5;28;01mimport\u001b[39;00m KNeighborsClassifier\n\u001b[32m---> \u001b[39m\u001b[32m11\u001b[39m knn_model_tuned = KNeighborsClassifier(n_neighbors = best_k) \u001b[38;5;66;03m# here, we use the best_k\u001b[39;00m\n\u001b[32m 12\u001b[39m \n\u001b[32m 13\u001b[39m \u001b[38;5;66;03m# we train the model (one line!)\u001b[39;00m\n\u001b[32m 14\u001b[39m knn_model_tuned.fit(X_train, y_train) \u001b[38;5;66;03m# yay! trained!\u001b[39;00m\n", + "\u001b[31mNameError\u001b[39m: name 'best_k' is not defined" + ] + } + ], "source": [ "################################################\n", "# BLOCK 30: RE-BUILD & RE-TRAIN USING BEST k\n", @@ -1371,11 +2614,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "metadata": { "id": "9h5-1TukKPSF" }, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'knn_model_tuned' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mNameError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[47]\u001b[39m\u001b[32m, line 8\u001b[39m\n\u001b[32m 4\u001b[39m \n\u001b[32m 5\u001b[39m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[32m 6\u001b[39m \u001b[38;5;66;03m# Re-create and re-run the \"Model-testing Cell\" How does it do with best_k?!\u001b[39;00m\n\u001b[32m 7\u001b[39m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m8\u001b[39m predicted_labels = knn_model_tuned.predict(X_test)\n\u001b[32m 9\u001b[39m actual_labels = y_test\n\u001b[32m 10\u001b[39m \n\u001b[32m 11\u001b[39m \u001b[38;5;66;03m# Let's print them so we can compare...\u001b[39;00m\n", + "\u001b[31mNameError\u001b[39m: name 'knn_model_tuned' is not defined" + ] + } + ], "source": [ "################################################\n", "# BLOCK 31: RE-TEST THE BEST-k MODEL\n", @@ -1503,11 +2758,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "metadata": { "id": "MlnIrLPWRQ58" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " sepallen sepalwid petallen petalwid irisname\n", + "50 7.0 3.2 4.7 1.4 1\n", + "51 6.4 3.2 4.5 1.5 1\n", + "52 6.9 3.1 4.9 1.5 1\n", + "53 5.5 2.3 4.0 1.3 1\n", + ".. ... ... ... ... ...\n", + "96 5.7 2.9 4.2 1.3 1\n", + "97 6.2 2.9 4.3 1.3 1\n", + "98 5.1 2.5 3.0 1.1 1\n", + "99 5.7 2.8 4.1 1.3 1\n", + "\n", + "[50 rows x 5 columns]\n", + "======================================================================\n", + "sepallen 5.936\n", + "sepalwid 2.770\n", + "petallen 4.260\n", + "petalwid 1.326\n", + "irisname 1.000\n", + "dtype: float64\n", + "======================================================================\n", + "[5.936 2.77 4.26 1.326]\n", + "predicted_species = [1.]\n", + "Mean versicolor features [5.936 2.77 4.26 1.326] -> versicolor (1)\n" + ] + } + ], "source": [ "################################################################\n", "# BLOCK 35b: HOW DOES THE MODEL PERFORM ON MEAN OF EACH FLOWER?\n", @@ -1529,7 +2814,9 @@ "# now run the predictive model (see Block 34 above) and print a corresponding\n", "# message\n", "\n", - "# >>> YOU ADD CODE HERE" + "# >>> YOU ADD CODE HERE\n", + "result = predictiveModel(versicolor_features_means)\n", + "print(f\"Mean versicolor features {versicolor_features_means} -> {result}\")" ] }, { @@ -1552,7 +2839,12 @@ "# (c) call the predictive model using those features\n", "# (d) print a corresponding message\n", "\n", - "# >> YOU ADD CODE HERE -- YOU SHOULD NEED NO MORE THAN 5 LINES OF CODE" + "# >> YOU ADD CODE HERE -- YOU SHOULD NEED NO MORE THAN 5 LINES OF CODE\n", + "for name in species_names:\n", + " species_data = df_final[df_final['irisname'] == convertSpecies(name)]\n", + " species_features_means = np.asarray(species_data.mean()[:-1])\n", + " result = predictiveModel(species_features_means)\n", + " print(f\"Mean {name} features {species_features_means} -> {result}\")" ] } ],