Merge branch 'main' of https://gitea.jany.se/Jany/MLPproject
This commit is contained in:
74
info.ipynb
74
info.ipynb
@@ -10,6 +10,7 @@
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"name": "stdout",
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"name": "stdout",
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"output_type": "stream",
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"output_type": "stream",
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"text": [
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"text": [
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<<<<<<< HEAD
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" age workclass education.num marital.status occupation \\\n",
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" age workclass education.num marital.status occupation \\\n",
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"0 90 0 9 6 0 \n",
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"0 90 0 9 6 0 \n",
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"1 82 4 9 6 4 \n",
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"1 82 4 9 6 4 \n",
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@@ -67,6 +68,66 @@
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"6 race 0.023650\n",
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"6 race 0.023650\n",
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"3 marital.status 0.011255\n",
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"3 marital.status 0.011255\n",
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"7 sex 0.006286\n"
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"7 sex 0.006286\n"
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=======
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" age workclass fnlwgt education.num marital.status \\\n",
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"0 90 ? 77053 9 Widowed \n",
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"1 82 Private 132870 9 Widowed \n",
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"2 66 ? 186061 10 Widowed \n",
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"3 54 Private 140359 4 Divorced \n",
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"4 41 Private 264663 10 Separated \n",
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"... ... ... ... ... ... \n",
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"32556 22 Private 310152 10 Never-married \n",
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"32557 27 Private 257302 12 Married-civ-spouse \n",
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"32558 40 Private 154374 9 Married-civ-spouse \n",
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"32559 58 Private 151910 9 Widowed \n",
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"32560 22 Private 201490 9 Never-married \n",
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"\n",
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" occupation relationship race sex capital.gain \\\n",
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"0 ? Not-in-family White Female 0 \n",
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"1 Exec-managerial Not-in-family White Female 0 \n",
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"2 ? Unmarried Black Female 0 \n",
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"3 Machine-op-inspct Unmarried White Female 0 \n",
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"4 Prof-specialty Own-child White Female 0 \n",
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"... ... ... ... ... ... \n",
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"32556 Protective-serv Not-in-family White Male 0 \n",
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"32557 Tech-support Wife White Female 0 \n",
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"32558 Machine-op-inspct Husband White Male 0 \n",
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"32559 Adm-clerical Unmarried White Female 0 \n",
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"32560 Adm-clerical Own-child White Male 0 \n",
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"\n",
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" capital.loss hours.per.week native.country income \n",
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"0 4356 40 United-States <=50K \n",
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"1 4356 18 United-States <=50K \n",
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"2 4356 40 United-States <=50K \n",
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"3 3900 40 United-States <=50K \n",
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"4 3900 40 United-States <=50K \n",
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"... ... ... ... ... \n",
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"32556 0 40 United-States <=50K \n",
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"32557 0 38 United-States <=50K \n",
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"32558 0 40 United-States >50K \n",
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"32559 0 40 United-States <=50K \n",
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"32560 0 20 United-States <=50K \n",
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"\n",
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"[32561 rows x 14 columns]\n"
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]
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},
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{
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"ename": "ValueError",
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"evalue": "could not convert string to float: '?'",
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"output_type": "error",
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"traceback": [
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"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
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"\u001b[31mValueError\u001b[39m Traceback (most recent call last)",
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"\u001b[32m/tmp/ipykernel_3669/1374041598.py\u001b[39m in \u001b[36m?\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m 37\u001b[39m print(X)\n\u001b[32m 38\u001b[39m \n\u001b[32m 39\u001b[39m \u001b[38;5;66;03m# Create and train the decision tree classifier\u001b[39;00m\n\u001b[32m 40\u001b[39m clf = DecisionTreeClassifier(random_state=\u001b[32m42\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m41\u001b[39m clf.fit(X, y)\n\u001b[32m 42\u001b[39m \n\u001b[32m 43\u001b[39m \u001b[38;5;66;03m# Visualize the tree\u001b[39;00m\n\u001b[32m 44\u001b[39m plt.figure(figsize=(\u001b[32m20\u001b[39m,\u001b[32m10\u001b[39m))\n",
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"\u001b[32m~/Documents/MLPproject/.venv/lib/python3.12/site-packages/sklearn/base.py\u001b[39m in \u001b[36m?\u001b[39m\u001b[34m(estimator, *args, **kwargs)\u001b[39m\n\u001b[32m 1361\u001b[39m skip_parameter_validation=(\n\u001b[32m 1362\u001b[39m prefer_skip_nested_validation \u001b[38;5;28;01mor\u001b[39;00m global_skip_validation\n\u001b[32m 1363\u001b[39m )\n\u001b[32m 1364\u001b[39m ):\n\u001b[32m-> \u001b[39m\u001b[32m1365\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m fit_method(estimator, *args, **kwargs)\n",
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"\u001b[32m~/Documents/MLPproject/.venv/lib/python3.12/site-packages/sklearn/tree/_classes.py\u001b[39m in \u001b[36m?\u001b[39m\u001b[34m(self, X, y, sample_weight, check_input)\u001b[39m\n\u001b[32m 1020\u001b[39m self : DecisionTreeClassifier\n\u001b[32m 1021\u001b[39m Fitted estimator.\n\u001b[32m 1022\u001b[39m \"\"\"\n\u001b[32m 1023\u001b[39m \n\u001b[32m-> \u001b[39m\u001b[32m1024\u001b[39m super()._fit(\n\u001b[32m 1025\u001b[39m X,\n\u001b[32m 1026\u001b[39m y,\n\u001b[32m 1027\u001b[39m sample_weight=sample_weight,\n",
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"\u001b[32m~/Documents/MLPproject/.venv/lib/python3.12/site-packages/sklearn/tree/_classes.py\u001b[39m in \u001b[36m?\u001b[39m\u001b[34m(self, X, y, sample_weight, check_input, missing_values_in_feature_mask)\u001b[39m\n\u001b[32m 248\u001b[39m check_X_params = dict(\n\u001b[32m 249\u001b[39m dtype=DTYPE, accept_sparse=\u001b[33m\"csc\"\u001b[39m, ensure_all_finite=\u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[32m 250\u001b[39m )\n\u001b[32m 251\u001b[39m check_y_params = dict(ensure_2d=\u001b[38;5;28;01mFalse\u001b[39;00m, dtype=\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[32m--> \u001b[39m\u001b[32m252\u001b[39m X, y = validate_data(\n\u001b[32m 253\u001b[39m self, X, y, validate_separately=(check_X_params, check_y_params)\n\u001b[32m 254\u001b[39m )\n\u001b[32m 255\u001b[39m \n",
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"\u001b[32m~/Documents/MLPproject/.venv/lib/python3.12/site-packages/sklearn/utils/validation.py\u001b[39m in \u001b[36m?\u001b[39m\u001b[34m(_estimator, X, y, reset, validate_separately, skip_check_array, **check_params)\u001b[39m\n\u001b[32m 2962\u001b[39m \u001b[38;5;66;03m# :(\u001b[39;00m\n\u001b[32m 2963\u001b[39m check_X_params, check_y_params = validate_separately\n\u001b[32m 2964\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[33m\"estimator\"\u001b[39m \u001b[38;5;28;01mnot\u001b[39;00m \u001b[38;5;28;01min\u001b[39;00m check_X_params:\n\u001b[32m 2965\u001b[39m check_X_params = {**default_check_params, **check_X_params}\n\u001b[32m-> \u001b[39m\u001b[32m2966\u001b[39m X = check_array(X, input_name=\u001b[33m\"X\"\u001b[39m, **check_X_params)\n\u001b[32m 2967\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[33m\"estimator\"\u001b[39m \u001b[38;5;28;01mnot\u001b[39;00m \u001b[38;5;28;01min\u001b[39;00m check_y_params:\n\u001b[32m 2968\u001b[39m check_y_params = {**default_check_params, **check_y_params}\n\u001b[32m 2969\u001b[39m y = check_array(y, input_name=\u001b[33m\"y\"\u001b[39m, **check_y_params)\n",
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"\u001b[32m~/Documents/MLPproject/.venv/lib/python3.12/site-packages/sklearn/utils/validation.py\u001b[39m in \u001b[36m?\u001b[39m\u001b[34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_writeable, force_all_finite, ensure_all_finite, ensure_non_negative, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[39m\n\u001b[32m 1050\u001b[39m )\n\u001b[32m 1051\u001b[39m array = xp.astype(array, dtype, copy=\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[32m 1052\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 1053\u001b[39m array = _asarray_with_order(array, order=order, dtype=dtype, xp=xp)\n\u001b[32m-> \u001b[39m\u001b[32m1054\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m ComplexWarning \u001b[38;5;28;01mas\u001b[39;00m complex_warning:\n\u001b[32m 1055\u001b[39m raise ValueError(\n\u001b[32m 1056\u001b[39m \u001b[33m\"Complex data not supported\\n{}\\n\"\u001b[39m.format(array)\n\u001b[32m 1057\u001b[39m ) \u001b[38;5;28;01mfrom\u001b[39;00m complex_warning\n",
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"\u001b[32m~/Documents/MLPproject/.venv/lib/python3.12/site-packages/sklearn/utils/_array_api.py\u001b[39m in \u001b[36m?\u001b[39m\u001b[34m(array, dtype, order, copy, xp, device)\u001b[39m\n\u001b[32m 753\u001b[39m \u001b[38;5;66;03m# Use NumPy API to support order\u001b[39;00m\n\u001b[32m 754\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m copy \u001b[38;5;28;01mis\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[32m 755\u001b[39m array = numpy.array(array, order=order, dtype=dtype)\n\u001b[32m 756\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m757\u001b[39m array = numpy.asarray(array, order=order, dtype=dtype)\n\u001b[32m 758\u001b[39m \n\u001b[32m 759\u001b[39m \u001b[38;5;66;03m# At this point array is a NumPy ndarray. We convert it to an array\u001b[39;00m\n\u001b[32m 760\u001b[39m \u001b[38;5;66;03m# container that is consistent with the input's namespace.\u001b[39;00m\n",
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"\u001b[32m~/Documents/MLPproject/.venv/lib/python3.12/site-packages/pandas/core/generic.py\u001b[39m in \u001b[36m?\u001b[39m\u001b[34m(self, dtype, copy)\u001b[39m\n\u001b[32m 2167\u001b[39m )\n\u001b[32m 2168\u001b[39m values = self._values\n\u001b[32m 2169\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m copy \u001b[38;5;28;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 2170\u001b[39m \u001b[38;5;66;03m# Note: branch avoids `copy=None` for NumPy 1.x support\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m2171\u001b[39m arr = np.asarray(values, dtype=dtype)\n\u001b[32m 2172\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 2173\u001b[39m arr = np.array(values, dtype=dtype, copy=copy)\n\u001b[32m 2174\u001b[39m \n",
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"\u001b[31mValueError\u001b[39m: could not convert string to float: '?'"
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>>>>>>> 2ff73d660ba62540b0a320049b9314f62843e037
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]
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]
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}
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}
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],
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],
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@@ -93,7 +154,11 @@
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" df_encoded[column] = label_encoder.fit_transform(df_encoded[column])\n",
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" df_encoded[column] = label_encoder.fit_transform(df_encoded[column])\n",
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"\n",
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"\n",
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"# Now properly separate features and target\n",
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"# Now properly separate features and target\n",
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<<<<<<< HEAD
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"X = df_encoded.drop(columns=['income', 'native.country', 'education','fnlwgt'])\n",
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"X = df_encoded.drop(columns=['income', 'native.country', 'education','fnlwgt'])\n",
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=======
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"X = df_encoded.drop(columns=['income', 'native.country', 'education'])\n",
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>>>>>>> 2ff73d660ba62540b0a320049b9314f62843e037
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"y = df_encoded['income']\n",
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"y = df_encoded['income']\n",
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"\n",
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"\n",
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"# Split the data\n",
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"# Split the data\n",
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@@ -120,7 +185,16 @@
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"})\n",
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"})\n",
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"print(\"\\nFeature Importances:\")\n",
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"print(\"\\nFeature Importances:\")\n",
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"print(importances.sort_values('importance', ascending=False))\n",
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"print(importances.sort_values('importance', ascending=False))\n",
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<<<<<<< HEAD
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"\n"
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"\n"
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=======
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"\n",
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"# Example prediction\n",
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"print(\"\\nPrediction for a new sample:\")\n",
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"new_sample = pd.DataFrame([[350, 0, 0, 0.09]], columns=X.columns) # Force=300, Coating=Yes, Defects=No, Moisture=0.05\n",
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"prediction = clf.predict(new_sample)\n",
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"print(f\"Will the rod break? {'Yes' if prediction[0] == 1 else 'No'}\")"
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>>>>>>> 2ff73d660ba62540b0a320049b9314f62843e037
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]
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]
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}
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}
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],
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],
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Reference in New Issue
Block a user