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yeah you got this chief

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LinlyBoi
2023-05-15 22:42:50 +03:00
parent 9c09d5649a
commit 413f7a8f1f
1 changed files with 20 additions and 80 deletions
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dwarves/Mining_HQ.ipynb
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@@ -1068,79 +1068,13 @@
},
{
"cell_type": "code",
"execution_count": 28,
"execution_count": 56,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<bound method NDFrame.head of Rank Name \\\n",
"19 20.0 Grand Theft Auto V \n",
"20 21.0 Grand Theft Auto V \n",
"21 22.0 Brain Age: Train Your Brain in Minutes a Day \n",
"24 25.0 Mario Kart 7 \n",
"25 26.0 Pokemon Diamond / Pearl Version \n",
"... ... ... \n",
"55090 55091.0 Strafe \n",
"55423 55424.0 Thumper \n",
"55490 55491.0 Travis Strikes Again: No More Heroes \n",
"55528 55529.0 TumbleSeed \n",
"55653 55654.0 Wheels of Aurelia \n",
"\n",
" Genre ESRB_Rating Platform Publisher \\\n",
"19 Action M PS3 Rockstar Games \n",
"20 Action M PS4 Rockstar Games \n",
"21 Misc E DS Nintendo \n",
"24 Racing E 3DS Nintendo \n",
"25 Role-Playing E DS Nintendo \n",
"... ... ... ... ... \n",
"55090 Shooter M PC Devolver Digital \n",
"55423 Music E10 NS Drool \n",
"55490 Action M NS Grasshopper Manufacture Inc. \n",
"55528 Action-Adventure E NS aeiowu \n",
"55653 Visual Novel T PS4 Mixedbag Srl \n",
"\n",
" Developer Critic_Score User_Score Total_Shipped \\\n",
"19 Rockstar North 1.0 9.507143 20.076667 \n",
"20 Rockstar North 1.0 9.528571 19.543333 \n",
"21 Nintendo SDD 1.0 9.550000 19.010000 \n",
"24 Nintendo EAD / Retro Studios 1.0 9.614286 18.110000 \n",
"25 Game Freak 1.0 9.635714 17.670000 \n",
"... ... ... ... ... \n",
"55090 Pixel Titans 1.0 8.000000 0.030000 \n",
"55423 Drool 1.0 9.300000 0.030000 \n",
"55490 Grasshopper Manufacture 1.0 8.309565 0.030000 \n",
"55528 Team TumbleSeed 1.0 7.747826 0.030000 \n",
"55653 Santa Ragione Srl 1.0 8.550000 0.030000 \n",
"\n",
" NA_Sales PAL_Sales Year bin_Critic_Score bin_value \\\n",
"19 6.370 9.850 2013.0 larg 8.5 \n",
"20 6.060 9.710 2014.0 larg 8.5 \n",
"21 6.295 9.288 2006.0 larg 8.5 \n",
"24 7.000 8.022 2011.0 larg 8.5 \n",
"25 7.235 7.600 2007.0 larg 8.5 \n",
"... ... ... ... ... ... \n",
"55090 0.000 0.000 2017.0 larg 8.5 \n",
"55423 0.000 0.000 2017.0 larg 8.5 \n",
"55490 0.000 0.000 2019.0 larg 8.5 \n",
"55528 0.000 0.000 2017.0 epik 5.5 \n",
"55653 0.000 0.000 2016.0 larg 8.5 \n",
"\n",
" Critic_Score_Norm Kmean_Labels my_channel \n",
"19 1.830687 4 NaN \n",
"20 2.083244 4 NaN \n",
"21 0.736276 4 NaN \n",
"24 0.820462 4 NaN \n",
"25 1.157204 4 NaN \n",
"... ... ... ... \n",
"55090 -0.021392 6 NaN \n",
"55423 1.493945 8 NaN \n",
"55490 0.652091 6 NaN \n",
"55528 -0.189763 6 NaN \n",
"55653 0.652091 6 NaN \n",
"\n",
"[6116 rows x 18 columns]>\n",
" Genre ESRB_Rating Platform NA_Sales\n",
"19 Action 3 17 750\n",
"20 Action 3 18 742\n",
@@ -1161,19 +1095,19 @@
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\hellom\\AppData\\Local\\Temp\\ipykernel_12888\\1695520614.py:18: SettingWithCopyWarning: \n",
"/tmp/ipykernel_8738/2203680914.py:18: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" to_be_nodes[\"NA_Sales\"] = le.fit_transform(gammas[\"NA_Sales\"])\n",
"C:\\Users\\hellom\\AppData\\Local\\Temp\\ipykernel_12888\\1695520614.py:19: SettingWithCopyWarning: \n",
"/tmp/ipykernel_8738/2203680914.py:19: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" to_be_nodes[\"ESRB_Rating\"] = le.fit_transform(gammas[\"ESRB_Rating\"])\n",
"C:\\Users\\hellom\\AppData\\Local\\Temp\\ipykernel_12888\\1695520614.py:20: SettingWithCopyWarning: \n",
"/tmp/ipykernel_8738/2203680914.py:20: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
@@ -1189,7 +1123,7 @@
"gammas.loc[gammas[\"Critic_Score\"] < 5, 'Critic_Score'] = 0\n",
"gammas.loc[gammas[\"Critic_Score\"] > 5, 'Critic_Score'] = 1\n",
"\n",
"print(gammas.head)\n",
"# print(gammas.head)\n",
"\n",
"# Columns to be considered as nodes\n",
"to_be_nodes = gammas[node_cols]\n",
@@ -1197,9 +1131,9 @@
"\n",
"le = preprocessing.LabelEncoder()\n",
"# Attribute to be predicted\n",
"predikt_col = le.fit_transform(gammas[\"Genre\"])\n",
"predikt_col = le.fit_transform(gammas[\"Critic_Score\"])\n",
"\n",
"# to_be_nodes[\"Genre\"] = le.fit_transform(gammas[\"Genre\"])\n",
"to_be_nodes[\"Genre\"] = le.fit_transform(gammas[\"Genre\"])\n",
"to_be_nodes[\"NA_Sales\"] = le.fit_transform(gammas[\"NA_Sales\"])\n",
"to_be_nodes[\"ESRB_Rating\"] = le.fit_transform(gammas[\"ESRB_Rating\"])\n",
"to_be_nodes[\"Platform\"] = le.fit_transform(gammas[\"Platform\"])\n",
@@ -1231,18 +1165,24 @@
},
{
"cell_type": "code",
"execution_count": 25,
"execution_count": 54,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'DecisionTreeClassifier' is not defined",
"ename": "ValueError",
"evalue": "could not convert string to float: 'Action-Adventure'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[25], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m classifier_obj \u001b[39m=\u001b[39m DecisionTreeClassifier()\n\u001b[0;32m 3\u001b[0m classifier_obj \u001b[39m=\u001b[39m classifier_obj\u001b[39m.\u001b[39mfit(node_train, predikt_train)\n\u001b[0;32m 5\u001b[0m predikt_result \u001b[39m=\u001b[39m classifier_obj\u001b[39m.\u001b[39mpredict(node_test)\n",
"\u001b[1;31mNameError\u001b[0m: name 'DecisionTreeClassifier' is not defined"
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[54], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m classifier_obj \u001b[39m=\u001b[39m DecisionTreeClassifier()\n\u001b[0;32m----> 3\u001b[0m classifier_obj \u001b[39m=\u001b[39m classifier_obj\u001b[39m.\u001b[39;49mfit(node_train, predikt_train)\n\u001b[1;32m 5\u001b[0m predikt_result \u001b[39m=\u001b[39m classifier_obj\u001b[39m.\u001b[39mpredict(node_test)\n\u001b[1;32m 7\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mACCURACY FOR MODEL PRE: \u001b[39m\u001b[39m\"\u001b[39m, metrics\u001b[39m.\u001b[39maccuracy_score(predikt_test, predikt_result))\n",
"File \u001b[0;32m~/bin/anaconda3/envs/jewpidor/lib/python3.10/site-packages/sklearn/tree/_classes.py:889\u001b[0m, in \u001b[0;36mDecisionTreeClassifier.fit\u001b[0;34m(self, X, y, sample_weight, check_input)\u001b[0m\n\u001b[1;32m 859\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mfit\u001b[39m(\u001b[39mself\u001b[39m, X, y, sample_weight\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, check_input\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m):\n\u001b[1;32m 860\u001b[0m \u001b[39m \u001b[39m\u001b[39m\"\"\"Build a decision tree classifier from the training set (X, y).\u001b[39;00m\n\u001b[1;32m 861\u001b[0m \n\u001b[1;32m 862\u001b[0m \u001b[39m Parameters\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 886\u001b[0m \u001b[39m Fitted estimator.\u001b[39;00m\n\u001b[1;32m 887\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 889\u001b[0m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49mfit(\n\u001b[1;32m 890\u001b[0m X,\n\u001b[1;32m 891\u001b[0m y,\n\u001b[1;32m 892\u001b[0m sample_weight\u001b[39m=\u001b[39;49msample_weight,\n\u001b[1;32m 893\u001b[0m check_input\u001b[39m=\u001b[39;49mcheck_input,\n\u001b[1;32m 894\u001b[0m )\n\u001b[1;32m 895\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\n",
"File \u001b[0;32m~/bin/anaconda3/envs/jewpidor/lib/python3.10/site-packages/sklearn/tree/_classes.py:186\u001b[0m, in \u001b[0;36mBaseDecisionTree.fit\u001b[0;34m(self, X, y, sample_weight, check_input)\u001b[0m\n\u001b[1;32m 184\u001b[0m check_X_params \u001b[39m=\u001b[39m \u001b[39mdict\u001b[39m(dtype\u001b[39m=\u001b[39mDTYPE, accept_sparse\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mcsc\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m 185\u001b[0m check_y_params \u001b[39m=\u001b[39m \u001b[39mdict\u001b[39m(ensure_2d\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m, dtype\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m)\n\u001b[0;32m--> 186\u001b[0m X, y \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_validate_data(\n\u001b[1;32m 187\u001b[0m X, y, validate_separately\u001b[39m=\u001b[39;49m(check_X_params, check_y_params)\n\u001b[1;32m 188\u001b[0m )\n\u001b[1;32m 189\u001b[0m \u001b[39mif\u001b[39;00m issparse(X):\n\u001b[1;32m 190\u001b[0m X\u001b[39m.\u001b[39msort_indices()\n",
"File \u001b[0;32m~/bin/anaconda3/envs/jewpidor/lib/python3.10/site-packages/sklearn/base.py:549\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[0;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[1;32m 547\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mestimator\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m check_X_params:\n\u001b[1;32m 548\u001b[0m check_X_params \u001b[39m=\u001b[39m {\u001b[39m*\u001b[39m\u001b[39m*\u001b[39mdefault_check_params, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mcheck_X_params}\n\u001b[0;32m--> 549\u001b[0m X \u001b[39m=\u001b[39m check_array(X, input_name\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mX\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mcheck_X_params)\n\u001b[1;32m 550\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mestimator\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m check_y_params:\n\u001b[1;32m 551\u001b[0m check_y_params \u001b[39m=\u001b[39m {\u001b[39m*\u001b[39m\u001b[39m*\u001b[39mdefault_check_params, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mcheck_y_params}\n",
"File \u001b[0;32m~/bin/anaconda3/envs/jewpidor/lib/python3.10/site-packages/sklearn/utils/validation.py:877\u001b[0m, in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[1;32m 875\u001b[0m array \u001b[39m=\u001b[39m xp\u001b[39m.\u001b[39mastype(array, dtype, copy\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m)\n\u001b[1;32m 876\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 877\u001b[0m array \u001b[39m=\u001b[39m _asarray_with_order(array, order\u001b[39m=\u001b[39;49morder, dtype\u001b[39m=\u001b[39;49mdtype, xp\u001b[39m=\u001b[39;49mxp)\n\u001b[1;32m 878\u001b[0m \u001b[39mexcept\u001b[39;00m ComplexWarning \u001b[39mas\u001b[39;00m complex_warning:\n\u001b[1;32m 879\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m 880\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mComplex data not supported\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m{}\u001b[39;00m\u001b[39m\\n\u001b[39;00m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(array)\n\u001b[1;32m 881\u001b[0m ) \u001b[39mfrom\u001b[39;00m \u001b[39mcomplex_warning\u001b[39;00m\n",
"File \u001b[0;32m~/bin/anaconda3/envs/jewpidor/lib/python3.10/site-packages/sklearn/utils/_array_api.py:185\u001b[0m, in \u001b[0;36m_asarray_with_order\u001b[0;34m(array, dtype, order, copy, xp)\u001b[0m\n\u001b[1;32m 182\u001b[0m xp, _ \u001b[39m=\u001b[39m get_namespace(array)\n\u001b[1;32m 183\u001b[0m \u001b[39mif\u001b[39;00m xp\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m \u001b[39min\u001b[39;00m {\u001b[39m\"\u001b[39m\u001b[39mnumpy\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mnumpy.array_api\u001b[39m\u001b[39m\"\u001b[39m}:\n\u001b[1;32m 184\u001b[0m \u001b[39m# Use NumPy API to support order\u001b[39;00m\n\u001b[0;32m--> 185\u001b[0m array \u001b[39m=\u001b[39m numpy\u001b[39m.\u001b[39;49masarray(array, order\u001b[39m=\u001b[39;49morder, dtype\u001b[39m=\u001b[39;49mdtype)\n\u001b[1;32m 186\u001b[0m \u001b[39mreturn\u001b[39;00m xp\u001b[39m.\u001b[39masarray(array, copy\u001b[39m=\u001b[39mcopy)\n\u001b[1;32m 187\u001b[0m \u001b[39melse\u001b[39;00m:\n",
"File \u001b[0;32m~/bin/anaconda3/envs/jewpidor/lib/python3.10/site-packages/pandas/core/generic.py:2070\u001b[0m, in \u001b[0;36mNDFrame.__array__\u001b[0;34m(self, dtype)\u001b[0m\n\u001b[1;32m 2069\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__array__\u001b[39m(\u001b[39mself\u001b[39m, dtype: npt\u001b[39m.\u001b[39mDTypeLike \u001b[39m|\u001b[39m \u001b[39mNone\u001b[39;00m \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m np\u001b[39m.\u001b[39mndarray:\n\u001b[0;32m-> 2070\u001b[0m \u001b[39mreturn\u001b[39;00m np\u001b[39m.\u001b[39;49masarray(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_values, dtype\u001b[39m=\u001b[39;49mdtype)\n",
"\u001b[0;31mValueError\u001b[0m: could not convert string to float: 'Action-Adventure'"
]
}
],