problem with auto_ml

014.ipynb

{
  "cells": [
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import pandas as pd\nimport pandas_profiling\n\nimport numpy as np\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.metrics import mean_squared_error\n\nimport matplotlib.pyplot as plt\n\n%matplotlib inline",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# https://www.kaggle.com/dansbecker/new-york-city-taxi-fare-prediction\ntaxi_data = pd.read_csv(\"../input/taxi.zip\")",
      "execution_count": 2,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "taxi_data[\"pickup_datetime\"] = pd.to_datetime(taxi_data[\"pickup_datetime\"])\n\ntaxi_data[\"pickup_year\"] = taxi_data[\"pickup_datetime\"].dt.year\ntaxi_data[\"pickup_month\"] = taxi_data[\"pickup_datetime\"].dt.month\ntaxi_data[\"pickup_day\"] = taxi_data[\"pickup_datetime\"].dt.day\ntaxi_data[\"pickup_hour\"] = taxi_data[\"pickup_datetime\"].dt.hour\ntaxi_data[\"pickup_minute\"] = taxi_data[\"pickup_datetime\"].dt.minute\ntaxi_data[\"pickup_second\"] = taxi_data[\"pickup_datetime\"].dt.second\ntaxi_data[\"pickup_dayofweek\"] = taxi_data[\"pickup_datetime\"].dt.dayofweek\n\ntaxi_data.drop([\"key\", \"pickup_datetime\"], axis = 1, inplace = True)\n\ntaxi_data[\"longitude_diff\"] = abs(taxi_data[\"pickup_longitude\"] - taxi_data[\"dropoff_longitude\"])\ntaxi_data[\"latitude_diff\"] = abs(taxi_data[\"pickup_latitude\"] - taxi_data[\"dropoff_latitude\"])\n\ntaxi_data[\"euclidean_dist\"] = np.sqrt((taxi_data[\"pickup_longitude\"] - taxi_data[\"dropoff_longitude\"])**2 + \n                                      (taxi_data[\"pickup_latitude\"] - taxi_data[\"dropoff_latitude\"])**2)\ntaxi_data[\"taxicab_dist\"] = taxi_data[\"longitude_diff\"] + taxi_data[\"latitude_diff\"]",
      "execution_count": 3,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "y = taxi_data[\"fare_amount\"]\nX = taxi_data.drop([\"fare_amount\"], axis=1)",
      "execution_count": 4,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "train, test, X_train, X_test, y_train, y_test = train_test_split(taxi_data, X, y, test_size = 0.25, random_state = 42)",
      "execution_count": 5,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "train.shape, test.shape, X_train.shape, X_test.shape, y_train.shape, y_test.shape ",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 6,
          "data": {
            "text/plain": "((37500, 17), (12500, 17), (37500, 16), (12500, 16), (37500,), (12500,))"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "from auto_ml import Predictor",
      "execution_count": 7,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "column_descriptions = {\n  'fare_amount': 'output'\n}",
      "execution_count": 8,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "ml_predictor = Predictor(type_of_estimator='regressor', column_descriptions=column_descriptions)",
      "execution_count": 9,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "#ml_predictor.train(train)\n#ml_predictor.train(train, model_names=[\"DeepLearningRegressor\"])\n#ml_predictor.train(train, model_names=[\"XGBRegressor\"])\n#ml_predictor.train(train, model_names=[\"LGBMRegressor\"])\n#ml_predictor.train(train, model_names=[\"LGBMRegressor\", \"XGBRegressor\", \"DeepLearningRegressor\"])\nml_predictor.train(train, model_names=[\"ExtraTreesRegressor\", \"GradientBoostingRegressor\", \"AdaBoostRegressor\", \n                                       \"RandomForestRegressor\", \"XGBRegressor\"])",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Welcome to auto_ml! We're about to go through and make sense of your data using machine learning, and give you a production-ready pipeline to get predictions with.\n\nIf you have any issues, or new feature ideas, let us know at http://auto.ml\nYou are running on version 2.9.10\nNow using the model training_params that you passed in:\n{}\nAfter overwriting our defaults with your values, here are the final params that will be used to initialize the model:\n{'n_jobs': -1}\nRunning basic data cleaning\nFitting DataFrameVectorizer\nNow using the model training_params that you passed in:\n{}\nAfter overwriting our defaults with your values, here are the final params that will be used to initialize the model:\n{'n_jobs': -1}\n\n\n********************************************************************************************\nAbout to run GridSearchCV on the pipeline for several models to predict fare_amount\nFitting 2 folds for each of 5 candidates, totalling 10 fits\n",
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "'function' object has no attribute 'im_self'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-10-25a9cfb9103a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m#ml_predictor.train(train, model_names=[\"LGBMRegressor\", \"XGBRegressor\", \"DeepLearningRegressor\"])\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m ml_predictor.train(train, model_names=[\"ExtraTreesRegressor\", \"GradientBoostingRegressor\", \"AdaBoostRegressor\", \n\u001b[0;32m----> 7\u001b[0;31m                                        \"RandomForestRegressor\", \"XGBRegressor\"])\n\u001b[0m",
            "\u001b[0;32m~/miniconda3/envs/jakbadacdane.pl/lib/python3.6/site-packages/auto_ml/predictor.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m    668\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    669\u001b[0m         \u001b[0;31m# This is our main logic for how we train the final model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 670\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrained_final_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_ml_estimator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_names\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_scorer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_df\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    671\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    672\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensemble_config\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensemble_config\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/miniconda3/envs/jakbadacdane.pl/lib/python3.6/site-packages/auto_ml/predictor.py\u001b[0m in \u001b[0;36mtrain_ml_estimator\u001b[0;34m(self, estimator_names, scoring, X_df, y, feature_learning, prediction_interval)\u001b[0m\n\u001b[1;32m   1247\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrid_search_params\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgrid_search_params\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1248\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1249\u001b[0;31m             \u001b[0mgscv_results\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_grid_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_df\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrid_search_params\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrefit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1250\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1251\u001b[0m             \u001b[0mtrained_final_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgscv_results\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_estimator_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/miniconda3/envs/jakbadacdane.pl/lib/python3.6/site-packages/auto_ml/predictor.py\u001b[0m in \u001b[0;36mfit_grid_search\u001b[0;34m(self, X_df, y, gs_params, feature_learning, refit)\u001b[0m\n\u001b[1;32m   1192\u001b[0m                 \u001b[0;31m# Note that we will only report analytics results on the final model that ultimately gets selected, and trained on the entire dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1193\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1194\u001b[0;31m         \u001b[0mgs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_df\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1195\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1196\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/miniconda3/envs/jakbadacdane.pl/lib/python3.6/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[1;32m    637\u001b[0m                                   error_score=self.error_score)\n\u001b[1;32m    638\u001b[0m           for parameters, (train, test) in product(candidate_params,\n\u001b[0;32m--> 639\u001b[0;31m                                                    cv.split(X, y, groups)))\n\u001b[0m\u001b[1;32m    640\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    641\u001b[0m         \u001b[0;31m# if one choose to see train score, \"out\" will contain train score info\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/miniconda3/envs/jakbadacdane.pl/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m    787\u001b[0m                 \u001b[0;31m# consumption.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    788\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 789\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    790\u001b[0m             \u001b[0;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    791\u001b[0m             \u001b[0melapsed_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_start_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/miniconda3/envs/jakbadacdane.pl/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    697\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    698\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'supports_timeout'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 699\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    700\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    701\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/miniconda3/envs/jakbadacdane.pl/lib/python3.6/multiprocessing/pool.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    642\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    643\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 644\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    645\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    646\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_set\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/miniconda3/envs/jakbadacdane.pl/lib/python3.6/multiprocessing/pool.py\u001b[0m in \u001b[0;36m_handle_tasks\u001b[0;34m(taskqueue, put, outqueue, pool, cache)\u001b[0m\n\u001b[1;32m    422\u001b[0m                         \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    423\u001b[0m                     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 424\u001b[0;31m                         \u001b[0mput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    425\u001b[0m                     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    426\u001b[0m                         \u001b[0mjob\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtask\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/miniconda3/envs/jakbadacdane.pl/lib/python3.6/site-packages/sklearn/externals/joblib/pool.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(obj)\u001b[0m\n\u001b[1;32m    369\u001b[0m             \u001b[0;32mdef\u001b[0m \u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    370\u001b[0m                 \u001b[0mbuffer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 371\u001b[0;31m                 \u001b[0mCustomizablePickler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reducers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    372\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_writer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend_bytes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuffer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    373\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/miniconda3/envs/jakbadacdane.pl/lib/python3.6/site-packages/auto_ml/predictor.py\u001b[0m in \u001b[0;36m_pickle_method\u001b[0;34m(m)\u001b[0m\n\u001b[1;32m     47\u001b[0m \u001b[0;31m# For handling parallelism edge cases\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_pickle_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 49\u001b[0;31m     \u001b[0;32mif\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mim_self\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     50\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mim_class\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mim_func\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunc_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'function' object has no attribute 'im_self'"
          ]
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "test_score = ml_predictor.score(test, test.fare_amount)",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "conda-env-jakbadacdane.pl-py",
      "display_name": "Python [conda env:jakbadacdane.pl]",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.6.6",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "gist": {
      "id": "",
      "data": {
        "description": "problem with auto_ml",
        "public": true
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
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