├── .gitignore ├── README.md ├── test ├── test.py └── rfImputer_demo.ipynb └── module └── rfImputer.py /.gitignore: -------------------------------------------------------------------------------- 1 | *.pyc 2 | .ipynb_checkpoints/ 3 | *.csv 4 | *.dat -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # rfImputer 2 | An automatic random forest missing value imputer 3 | 4 | Loosely following the algorith described in [Stekhoven and Bühlmann (2011)](http://bioinformatics.oxfordjournals.org/content/28/1/112.short). 5 | -------------------------------------------------------------------------------- /test/test.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import sys 3 | sys.path.insert(1, '../') 4 | from rfImputer import rfImputer 5 | import numpy as np 6 | from pprint import pprint 7 | cols = ['Proof_cut', 'Viscosity', 'Caliper', 'Ink_temperature', 'Humifity', 8 | 'Roughness', 'Blade_pressure', 'Varnish_pct', 'Press_speed', 'Ink_pct', 9 | 'Solvent_pct', 'Esa_voltage', 'ESA_amperage', 'Wax', 'Hardener', 10 | 'Roller_durometer', 'Density', 'Anode_ratio', 'Chrome_content', 'bands'] 11 | df = pd.read_csv('bands.csv') 12 | df.columns = cols 13 | 14 | df.replace('?', 'NaN', inplace = True) 15 | df.replace('band', '1', inplace = True) 16 | df.replace('noband', '0', inplace = True) 17 | 18 | for col in df: 19 | if col == 'bands': 20 | continue 21 | df[col] = df[col].astype(float) 22 | 23 | # Generate fake data 24 | # n = 100 25 | # df = pd.DataFrame(index = np.arange(0, n)) 26 | # df['cont_3'] = np.random.randn(n) 27 | # df['cont_4'] = np.random.randn(n) 28 | # df['cat_1'] = pd.Categorical(np.random.randint(0, 2, n)) 29 | # df['cat_2'] = pd.Categorical(np.random.randint(0, 2, n)) 30 | # df['cat_3'] = pd.Categorical(np.random.randint(0, 2, n)) 31 | # df['cat_4'] = pd.Categorical(np.random.randint(0, 2, n)) 32 | # df['cont_1'] = np.random.randn(n) + 0.5 * df['cat_2'] + df['cat_3'] 33 | # df['cont_2'] = np.random.randn(n) + 0.5 * df['cont_1'] + df['cat_1'] 34 | 35 | #Fill in some more missing data 36 | # n = df.shape[0] 37 | # for col in df.columns: 38 | # n_missing = np.random.randint(low = 0, high = n/4, size = 1)[0] 39 | # missing_idx = np.random.choice(n, n_missing, replace = False) 40 | # df[col].iloc[missing_idx] = np.nan 41 | 42 | imp_df = rfImputer(df) 43 | 44 | imp_df.impute('random_forest', {'n_estimators': 100, 'n_jobs': 1}) 45 | print "IMPUTED" 46 | 47 | out = imp_df.imputed_df() 48 | 49 | print out.head() 50 | 51 | -------------------------------------------------------------------------------- /module/rfImputer.py: -------------------------------------------------------------------------------- 1 | # Take a dataset and impute values for each variable using a random 2 | # forest with all other variables as predictors 3 | 4 | from sklearn.ensemble import RandomForestClassifier 5 | from sklearn.ensemble import RandomForestRegressor 6 | from sklearn.preprocessing import Imputer 7 | import pandas as pd 8 | import numpy as np 9 | import copy 10 | 11 | class rfImputer(object): 12 | 13 | """Random Forest Imputer Class 14 | 15 | Attributes: 16 | ----------- 17 | 18 | data (pandas.DataFrame): The original data. 19 | 20 | missing (dict): Dictionary of lists of indices of missing values by column. 21 | 22 | prop_missing (dict): Proportion of missing values per column. 23 | 24 | imputed_values (dict): Dictionary of lists of imputed values. Only available 25 | after `impute()` method has been applied to instance. 26 | 27 | imputation_scores (dict): Out-of-bag accuracy scores of the random forest 28 | model for each variable. Only available after the `impute()` method 29 | with `imputation_method = 'random_forest'` has been applied to instance. 30 | 31 | incl_impute (list): Column names of variables to include in imputation. 32 | Can be set by user (see `__init__()`) 33 | 34 | incl_predict (list): Column names of variables to include as predictors 35 | in the random forest models (see `__init__()`). 36 | 37 | col_types (dict): The data type of each column (regression or classification) 38 | which indicates if `RandomForestClassifier` or `RandomForestRegressor` is 39 | used for imputation of respective variable. 40 | 41 | 42 | __init(self, data, **kwargs)__ 43 | 44 | 45 | Args: 46 | ---------- 47 | 48 | data (pandas.DataFrame): Original data. See README for format. 49 | 50 | incl_impute (list)[optional]: Column names of variables to include in 51 | imputation. Only if `excl_impute` is not specified. I neither is given 52 | all columns in the data frame are imputed. 53 | 54 | excl_impute (list)[optional]: Column names of variables to exclude from 55 | imputation. All other columns are used. Only if `incl_impute` is not 56 | specified. I neither is given all columns in the data frame are imputed. 57 | 58 | incl_predict (list)[optional]: Column names of variables to use as predictors 59 | in imputation model. Only if `excl_predict` is not specified. I 60 | neither is given all columns in the data frame are imputed. 61 | 62 | excl_predict (list)[optional]: Column names of variables not to use as 63 | predictors in imputation model. Only if `incl_predict` is not specified. 64 | If neither is given all columns in the data frame are imputed. 65 | 66 | is_classification (list)[optional]: Columns that are classification tasks. 67 | All columns that are not specified in `is_classification` or 68 | `is_regression` are automatically detected. The detection is not well 69 | implemented yet. 70 | 71 | is_regression (list)[optional]: Columns that are regression. Columns that are classification tasks. 72 | All columns that are not specified in `is_classification` or 73 | `is_regression` are automatically detected. The detection is not well 74 | implemented yet. 75 | """ 76 | 77 | def __init__(self, data, **kwargs): 78 | self.data = data 79 | self.missing = self.find_missing() 80 | self.prop_missing = self.prop_missing() 81 | self.imputed_values = {} 82 | self.imputation_scores = {} 83 | 84 | # Columns in which values should be imputed 85 | if 'incl_impute' in kwargs: 86 | self.incl_impute = kwargs['incl_impute'] 87 | elif 'excl_impute' in kwargs: 88 | self.incl_impute = [c for c in data.columns if c not in kwargs['excl_impute']] 89 | else: 90 | self.incl_impute = data.columns 91 | 92 | # Columns that should be used as predictors for the imputation 93 | if 'incl_predict' in kwargs: 94 | self.incl_predict = kwargs['incl_predict'] 95 | elif 'excl_predict' in kwargs: 96 | self.incl_predict = [c for c in data.columns if c not in kwargs['excl_predict']] 97 | elif 'incl_predict' in kwargs and 'excl_predict' in kwargs: 98 | raise ValueError("Specify either excl_predict or incl_predict") 99 | else: 100 | self.incl_predict = data.columns 101 | 102 | ## Column types 103 | 104 | # Manually specified column types 105 | try: 106 | self.is_classification = kwargs['is_classification'] 107 | except KeyError: 108 | self.is_classification = [] 109 | try: 110 | self.is_regression = kwargs['is_regression'] 111 | except KeyError: 112 | self.is_regression = [] 113 | 114 | # Detect unspecified column types 115 | self.col_types = self.assign_dtypes() 116 | 117 | 118 | def detect_dtype(self, x): 119 | """ 120 | Detect if variable requires classification or regression. 121 | 122 | Args: 123 | ---------- 124 | x (pandas.Series): Column of data frame to detect type for 125 | 126 | 127 | Returns: 128 | ---------- 129 | str: Either 'classification' or 'regression' 130 | 131 | Needs to be improved 132 | """ 133 | 134 | if x.dtype == 'float': 135 | if len(x.unique()) < 4: 136 | out = 'classification' 137 | else: 138 | out = 'regression' 139 | elif x.dtype == 'object': 140 | out = 'classification' 141 | elif x.dtype == 'int64': 142 | if len(x.unique()) < 4: 143 | out = 'classification' 144 | else: 145 | out = 'regression' 146 | else: 147 | msg = 'Unrecognized data type: %s' %x.dtype 148 | raise ValueError(msg) 149 | 150 | return out 151 | 152 | def assign_dtypes(self): 153 | """ 154 | Assign prespecified and detect non specified column types 155 | """ 156 | dtypes = {} 157 | for var in self.data.columns: 158 | if var in self.is_classification: 159 | dtypes[var] = 'classification' 160 | elif var in self.is_regression: 161 | dtypes[var] = 'regression' 162 | else: 163 | dtypes[var] = self.detect_dtype(self.data[var]) 164 | 165 | return dtypes 166 | 167 | def find_missing(self): 168 | """ 169 | Find the indices of missing data in the original data frame 170 | 171 | Args: 172 | --------- 173 | 174 | Returns: 175 | ---------- 176 | dict: 'var_name': [missing indices] 177 | 178 | """ 179 | missing = {} 180 | for var in self.data.columns: 181 | col = self.data[var] 182 | missing[var] = col.index[col.isnull()] 183 | 184 | return missing 185 | 186 | def prop_missing(self): 187 | """ 188 | Calculate the proportion of missing values for each column in the original 189 | data 190 | 191 | Args: 192 | --------- 193 | 194 | Returns: 195 | --------- 196 | 197 | dict: 'var_name': float 198 | """ 199 | out = {} 200 | n = self.data.shape[0] 201 | for var in self.data.columns: 202 | out[var] = float(len(self.missing[var])) / float(n) 203 | return out 204 | 205 | 206 | def mean_mode_impute(self, var): 207 | 208 | """ 209 | Impute mean for continuous and mode for categorical and binary data 210 | 211 | Args: 212 | --------- 213 | var (str): Column name of variable in original data 214 | 215 | Returns: 216 | --------- 217 | np.array: Repeating mean or mode of `var` 218 | """ 219 | 220 | if self.col_types[var] == 'regression': 221 | statistic = self.data[var].mean() 222 | elif self.col_types[var] == 'classification': 223 | statistic = self.data[var].mode()[0] 224 | else: 225 | raise ValueError('Unknown data type') 226 | 227 | out = np.repeat(statistic, repeats = len(self.missing[var])) 228 | return out 229 | 230 | 231 | def rf_impute(self, impute_var, data_imputed, rf_params): 232 | """ 233 | Fit random forest and get predictions for missing values 234 | 235 | Args: 236 | ---------- 237 | 238 | impute_var (str): Column name of variable to be imputed 239 | 240 | data_imputed (pandas.DataFrame): A complete imputed DataFrame from the 241 | previous iteration 242 | 243 | rf_params (dict): Parameters for `RandomForestClassifier/Regressor`. Keys 244 | must be named arguments from the respective `sklearn` method 245 | 246 | 247 | Returns: 248 | ---------- 249 | Sets `scores` and `imputed_values` 250 | float: Accuracy score from `RandomForestClassifier/Regressor` 251 | np.array: Imputations from out of bag predictions 252 | 253 | """ 254 | 255 | y = data_imputed[impute_var] 256 | include = [x for x in self.incl_predict if x != impute_var] 257 | X = data_imputed[include] 258 | 259 | if self.col_types[impute_var] == 'classification': 260 | rf = RandomForestClassifier(oob_score = True, **rf_params) 261 | rf.fit(y = y, X = X) 262 | oob_predictions = np.argmax(rf.oob_decision_function_, axis = 1) 263 | oob_imputation = oob_predictions[self.missing[impute_var]] 264 | 265 | else: 266 | rf = RandomForestRegressor(oob_score = True, **rf_params) 267 | rf.fit(y = y, X = X) 268 | oob_imputation = rf.oob_prediction_[self.missing[impute_var]] 269 | 270 | self.imputation_scores[impute_var] = rf.oob_score_ 271 | self.imputed_values[impute_var] = oob_imputation 272 | 273 | 274 | def get_divergence(self, imputed_old): 275 | """ 276 | Calculate divergence to imputations from previous iteration 277 | 278 | Args: 279 | --------- 280 | imputed_old (dict): Imputated values from previous iteration. 281 | 282 | Returns: 283 | --------- 284 | float: divergence of categorical variables 285 | float: divergence of continuous variable 286 | """ 287 | 288 | # Calcualte continuous divergence 289 | div_cat = 0 290 | norm_cat = 0 291 | div_cont = 0 292 | norm_cont = 0 293 | for var in self.imputed_values: 294 | if self.col_types[var] == 'regression': 295 | div = imputed_old[var] - self.imputed_values[var] 296 | div_cont += div.dot(div) 297 | norm_cont += self.imputed_values[var].dot(self.imputed_values[var]) 298 | elif self.col_types[var] == 'classification': 299 | div = [1 if old != new 300 | else 0 301 | for old, new in zip(imputed_old[var], self.imputed_values[var])] 302 | div_cat += sum(div) 303 | norm_cat += len(div) 304 | else: 305 | raise ValueError("Unrecognized variable type") 306 | 307 | 308 | if norm_cat == 0: 309 | cat_out = 0 310 | else: 311 | cat_out = div_cat / norm_cat 312 | if norm_cont == 0: 313 | cont_out = 0 314 | else: 315 | cont_out = div_cont / norm_cont 316 | 317 | return cat_out, cont_out 318 | 319 | 320 | def impute(self, imputation_type, rf_params = None): 321 | """ 322 | Impute data for `rfImputer` object 323 | 324 | Imputed either mean/mode or predictions from random fores model. Iteratively 325 | fits random forest models until predictions for missing data converge. 326 | 327 | Args: 328 | ---------- 329 | imputation_type (str): `simple` for mean/mode imputation or 'random_forest' 330 | for random forest imputation. 331 | 332 | rf_params (dict): Parameters for sklearn methods. Must stick to naming 333 | conventions. 334 | 335 | Returns: 336 | ---------- 337 | Sets `imputed_values` 338 | 339 | """ 340 | 341 | if imputation_type == 'simple': 342 | for var in self.incl_impute: 343 | self.imputed_values[var] = self.mean_mode_impute(var) 344 | 345 | elif imputation_type == 'random_forest': 346 | 347 | print "-" * 50 348 | print "Starting Random Forest Imputation" 349 | print "-" * 50 350 | 351 | # Do a simple mean/mode imputation first 352 | 353 | for var in self.data.columns: 354 | self.imputed_values[var] = self.mean_mode_impute(var) 355 | 356 | # Rf Imputation Loop 357 | div_cat = float('inf') 358 | div_cont = float('inf') 359 | stop = False 360 | i = 0 361 | while not stop: 362 | 363 | i += 1 364 | print "Iteration %d:" %i 365 | print "."* 10 366 | # Store results from previous iteration 367 | imputations_old = copy.copy(self.imputed_values) 368 | div_cat_old = div_cat 369 | div_cont_old = div_cont 370 | 371 | # Make predictor matrix, using the imputed values 372 | data_imputed = self.imputed_df(output = False) 373 | 374 | for var in self.incl_impute: 375 | self.rf_impute(var, data_imputed, rf_params) 376 | 377 | div_cat, div_cont = self.get_divergence(imputations_old) 378 | 379 | print "Categorical divergence: %f" %div_cat 380 | print "Continuous divergence: %f" %div_cont 381 | 382 | # Check if stopping criterion is met 383 | if div_cat >= div_cat_old and div_cont >= div_cont_old: 384 | stop = True 385 | 386 | else: 387 | msg = 'Unrecognized imputation type: %s' %imputation_type 388 | raise ValueError(msg) 389 | 390 | 391 | 392 | def imputed_df(self, output = True): 393 | ''' 394 | Fills the missing values in the input data frame with the values stored 395 | in imputed_values and returns a new data frame (copy). 396 | 397 | Args: 398 | --------- 399 | output (bool): If `False` internal method fills all missing values. If 400 | `True` only the missing values for columns in `incl_impute` are filled. 401 | 402 | Returns: 403 | ---------- 404 | pandas.DataFrame: Filled dataframe 405 | 406 | ''' 407 | if len(self.imputed_values) == 0: 408 | raise ValueError('No imputed values available. Call impute() first') 409 | 410 | out_df = self.data.copy() 411 | if not output: 412 | iterator = self.data.columns 413 | else: 414 | iterator = self.incl_impute 415 | 416 | # for var in iterator: 417 | # for idx, imp in zip(self.missing[var], self.imputed_values[var]): 418 | # out_df[var].iloc[idx] = imp 419 | for var in iterator: 420 | out_df.loc[self.missing[var],var] = self.imputed_values[var] 421 | return out_df 422 | -------------------------------------------------------------------------------- /test/rfImputer_demo.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 46, 6 | "metadata": { 7 | "collapsed": false 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import pandas as pd\n", 12 | "import sys\n", 13 | "sys.path.insert(1, '../')\n", 14 | "from rfImputer import rfImputer\n", 15 | "from sklearn.preprocessing import Imputer\n", 16 | "from pprint import pprint\n", 17 | "\n", 18 | "cols = ['Proof_cut', 'Viscosity', 'Caliper', 'Ink_temperature', 'Humifity',\n", 19 | " 'Roughness', 'Blade_pressure', 'Varnish_pct', 'Press_speed', 'Ink_pct',\n", 20 | " 'Solvent_pct', 'Esa_voltage', 'ESA_amperage', 'Wax', 'Hardener',\n", 21 | " 'Roller_durometer', 'Density', 'Anode_ratio', 'Chrome_content', 'bands']\n", 22 | "df = pd.read_csv('bands.csv')\n", 23 | "df.columns = cols\n", 24 | "\n", 25 | "df.replace('?', 'NaN', inplace = True)\n", 26 | "df.replace('band', '1', inplace = True)\n", 27 | "df.replace('noband', '0', inplace = True)\n", 28 | "\n", 29 | "for col in df:\n", 30 | " if col == 'bands':\n", 31 | " continue\n", 32 | " df[col] = df[col].astype(float)" 33 | ] 34 | }, 35 | { 36 | "cell_type": "code", 37 | "execution_count": null, 38 | "metadata": { 39 | "collapsed": false, 40 | "scrolled": true 41 | }, 42 | "outputs": [], 43 | "source": [ 44 | "df" 45 | ] 46 | }, 47 | { 48 | "cell_type": "code", 49 | "execution_count": 39, 50 | "metadata": { 51 | "collapsed": false, 52 | "scrolled": true 53 | }, 54 | "outputs": [ 55 | { 56 | "data": { 57 | "text/html": [ 58 | "
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" 549 | ], 550 | "text/plain": [ 551 | " 0 1 2 3 4 5 6 7 8 9 \\\n", 552 | "0 55.000000 46 0.300 15.0 80 0.750000 20.000000 6.6 1900 54.9 \n", 553 | "1 62.000000 40 0.433 16.0 80 0.724238 30.000000 6.5 1850 53.8 \n", 554 | "2 52.000000 40 0.300 16.0 75 0.312000 30.000000 5.6 1467 55.6 \n", 555 | "3 50.000000 46 0.300 17.0 80 0.750000 30.000000 0.0 2100 57.5 \n", 556 | "4 50.000000 40 0.267 16.8 76 0.438000 28.000000 8.6 1467 53.8 \n", 557 | "5 50.000000 46 0.300 16.5 75 0.750000 30.000000 0.0 2600 62.5 \n", 558 | "6 50.000000 46 0.200 16.5 75 0.750000 28.000000 0.0 2600 62.5 \n", 559 | "7 50.000000 45 0.367 12.0 70 0.750000 60.000000 0.0 1650 60.2 \n", 560 | "8 65.000000 43 0.333 16.0 75 1.000000 32.000000 22.7 1750 45.5 \n", 561 | "9 65.000000 43 0.200 16.0 68 0.750000 30.000000 15.5 1750 48.5 \n", 562 | "10 50.000000 50 0.367 14.0 80 0.750000 40.000000 10.5 1700 52.6 \n", 563 | "11 50.000000 50 0.300 15.0 70 1.000000 30.000000 10.0 1600 50.0 \n", 564 | "12 40.000000 45 0.300 14.5 70 0.625000 25.000000 0.0 1500 59.5 \n", 565 | "13 50.000000 43 0.267 16.0 75 1.000000 20.000000 15.8 1600 49.5 \n", 566 | "14 45.015496 45 0.233 15.0 87 1.000000 30.930526 0.0 1400 62.5 \n", 567 | "15 30.000000 45 0.200 14.0 65 0.724238 30.930526 0.0 1600 62.5 \n", 568 | "16 30.000000 45 0.200 15.5 65 0.724238 30.930526 5.9 1600 58.8 \n", 569 | "17 60.000000 38 0.267 16.4 64 0.750000 30.930526 11.0 1400 54.9 \n", 570 | "18 60.000000 38 0.333 16.5 66 0.750000 30.930526 6.7 1400 56.2 \n", 571 | "19 50.000000 45 0.233 16.0 70 0.812000 30.000000 0.0 2400 58.8 \n", 572 | "\n", 573 | " 10 11 12 13 14 15 16 17 18 19 \n", 574 | "0 38.5 0.0 0 2.5 0.7 34 40 105.00 100 0 \n", 575 | "1 39.8 0.0 0 2.8 0.9 40 40 103.87 100 0 \n", 576 | "2 38.8 0.0 0 2.5 1.3 40 40 108.06 100 0 \n", 577 | "3 42.5 5.0 0 2.3 0.6 35 40 106.67 100 0 \n", 578 | "4 37.6 5.0 0 2.5 0.8 40 40 103.87 100 0 \n", 579 | "5 37.5 6.0 0 2.5 0.6 30 40 106.67 100 0 \n", 580 | "6 37.5 6.0 0 2.5 1.1 30 40 106.67 100 0 \n", 581 | "7 39.8 1.5 0 3.0 1.0 40 40 103.22 100 1 \n", 582 | "8 31.8 0.0 0 3.0 1.0 38 40 106.66 100 0 \n", 583 | "9 35.9 0.0 0 3.0 1.0 38 40 106.60 100 0 \n", 584 | "10 36.8 0.0 0 2.5 1.0 38 40 105.00 100 1 \n", 585 | "11 40.0 0.0 0 2.8 1.0 38 40 106.66 100 1 \n", 586 | "12 40.5 0.0 0 2.0 1.0 40 40 100.00 100 1 \n", 587 | "13 34.7 0.0 0 2.5 1.0 38 40 103.22 100 0 \n", 588 | "14 37.5 0.0 0 2.5 1.0 40 40 103.22 100 1 \n", 589 | "15 37.5 0.0 0 2.5 0.8 33 40 100.00 100 0 \n", 590 | "16 35.3 0.0 0 2.5 1.8 33 40 100.00 100 0 \n", 591 | "17 34.1 0.0 0 2.5 1.0 32 40 103.22 100 0 \n", 592 | "18 37.1 0.0 0 1.1 1.4 32 40 103.22 100 0 \n", 593 | "19 41.2 0.0 0 1.7 1.0 30 40 107.40 100 0 " 594 | ] 595 | }, 596 | "execution_count": 39, 597 | "metadata": {}, 598 | "output_type": "execute_result" 599 | } 600 | ], 601 | "source": [ 602 | "imputer = Imputer(missing_values = 'NaN', strategy = 'mean', copy = False)\n", 603 | "imputer.fit(df)\n", 604 | "df_new = imputer.transform(df)\n", 605 | "pd.DataFrame(df_new).head(20)" 606 | ] 607 | }, 608 | { 609 | "cell_type": "code", 610 | "execution_count": 40, 611 | "metadata": { 612 | "collapsed": true 613 | }, 614 | "outputs": [], 615 | "source": [ 616 | "# Instantiate Imputer with data\n", 617 | "imp_df = rfImputer(df, is_classification = ['Density', 'Chrome_content'],\n", 618 | " is_regression = ['Anode_ratio'],\n", 619 | " excl_impute = ['Caliper', 'Ink_pct'], # or incl_impute = \n", 620 | " incl_predict = ['Press_speed', 'Solvent_pct', 'Chrome_content']) # or excl_predict = " 621 | ] 622 | }, 623 | { 624 | "cell_type": "code", 625 | "execution_count": 41, 626 | "metadata": { 627 | "collapsed": false 628 | }, 629 | "outputs": [ 630 | { 631 | "data": { 632 | "text/plain": [ 633 | "{'Anode_ratio': 'regression',\n", 634 | " 'Blade_pressure': 'regression',\n", 635 | " 'Caliper': 'regression',\n", 636 | " 'Chrome_content': 'classification',\n", 637 | " 'Density': 'classification',\n", 638 | " 'ESA_amperage': 'regression',\n", 639 | " 'Esa_voltage': 'regression',\n", 640 | " 'Hardener': 'regression',\n", 641 | " 'Humifity': 'regression',\n", 642 | " 'Ink_pct': 'regression',\n", 643 | " 'Ink_temperature': 'regression',\n", 644 | " 'Press_speed': 'regression',\n", 645 | " 'Proof_cut': 'regression',\n", 646 | " 'Roller_durometer': 'regression',\n", 647 | " 'Roughness': 'regression',\n", 648 | " 'Solvent_pct': 'regression',\n", 649 | " 'Varnish_pct': 'regression',\n", 650 | " 'Viscosity': 'regression',\n", 651 | " 'Wax': 'regression',\n", 652 | " 'bands': 'classification'}" 653 | ] 654 | }, 655 | "execution_count": 41, 656 | "metadata": {}, 657 | "output_type": "execute_result" 658 | } 659 | ], 660 | "source": [ 661 | "imp_df.col_types" 662 | ] 663 | }, 664 | { 665 | "cell_type": "code", 666 | "execution_count": 42, 667 | "metadata": { 668 | "collapsed": false 669 | }, 670 | "outputs": [ 671 | { 672 | "data": { 673 | "text/plain": [ 674 | "['Proof_cut',\n", 675 | " 'Viscosity',\n", 676 | " 'Ink_temperature',\n", 677 | " 'Humifity',\n", 678 | " 'Roughness',\n", 679 | " 'Blade_pressure',\n", 680 | " 'Varnish_pct',\n", 681 | " 'Press_speed',\n", 682 | " 'Solvent_pct',\n", 683 | " 'Esa_voltage',\n", 684 | " 'ESA_amperage',\n", 685 | " 'Wax',\n", 686 | " 'Hardener',\n", 687 | " 'Roller_durometer',\n", 688 | " 'Density',\n", 689 | " 'Anode_ratio',\n", 690 | " 'Chrome_content',\n", 691 | " 'bands']" 692 | ] 693 | }, 694 | "execution_count": 42, 695 | "metadata": {}, 696 | "output_type": "execute_result" 697 | } 698 | ], 699 | "source": [ 700 | "imp_df.incl_impute" 701 | ] 702 | }, 703 | { 704 | "cell_type": "code", 705 | "execution_count": 43, 706 | "metadata": { 707 | "collapsed": false 708 | }, 709 | "outputs": [ 710 | { 711 | "data": { 712 | "text/plain": [ 713 | "['Press_speed', 'Solvent_pct', 'Chrome_content']" 714 | ] 715 | }, 716 | "execution_count": 43, 717 | "metadata": {}, 718 | "output_type": "execute_result" 719 | } 720 | ], 721 | "source": [ 722 | "imp_df.incl_predict" 723 | ] 724 | }, 725 | { 726 | "cell_type": "code", 727 | "execution_count": 44, 728 | "metadata": { 729 | "collapsed": true 730 | }, 731 | "outputs": [], 732 | "source": [ 733 | "imp_df = rfImputer(df)" 734 | ] 735 | }, 736 | { 737 | "cell_type": "code", 738 | "execution_count": 47, 739 | "metadata": { 740 | "collapsed": false 741 | }, 742 | "outputs": [ 743 | { 744 | "name": "stdout", 745 | "output_type": "stream", 746 | "text": [ 747 | "{'Anode_ratio': 0.013011152416356878,\n", 748 | " 'Blade_pressure': 0.1171003717472119,\n", 749 | " 'Caliper': 0.05018587360594796,\n", 750 | " 'Chrome_content': 0.0055762081784386614,\n", 751 | " 'Density': 0.013011152416356878,\n", 752 | " 'ESA_amperage': 0.10037174721189591,\n", 753 | " 'Esa_voltage': 0.10408921933085502,\n", 754 | " 'Hardener': 0.013011152416356878,\n", 755 | " 'Humifity': 0.0018587360594795538,\n", 756 | " 'Ink_pct': 0.10223048327137546,\n", 757 | " 'Ink_temperature': 0.0037174721189591076,\n", 758 | " 'Press_speed': 0.01858736059479554,\n", 759 | " 'Proof_cut': 0.10037174721189591,\n", 760 | " 'Roller_durometer': 0.10037174721189591,\n", 761 | " 'Roughness': 0.055762081784386616,\n", 762 | " 'Solvent_pct': 0.10223048327137546,\n", 763 | " 'Varnish_pct': 0.10223048327137546,\n", 764 | " 'Viscosity': 0.00929368029739777,\n", 765 | " 'Wax': 0.011152416356877323,\n", 766 | " 'bands': 0.0}\n", 767 | "{}\n", 768 | "{}\n" 769 | ] 770 | } 771 | ], 772 | "source": [ 773 | "# Print basic attributes\n", 774 | "pprint(imp_df.prop_missing)\n", 775 | "print imp_df.imputed_values\n", 776 | "print imp_df.imputation_scores" 777 | ] 778 | }, 779 | { 780 | "cell_type": "code", 781 | "execution_count": 48, 782 | "metadata": { 783 | "collapsed": false 784 | }, 785 | "outputs": [ 786 | { 787 | "data": { 788 | "text/html": [ 789 | "
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Proof_cutViscosityCaliperInk_temperatureHumifityRoughnessBlade_pressureVarnish_pctPress_speedInk_pctSolvent_pctEsa_voltageESA_amperageWaxHardenerRoller_durometerDensityAnode_ratioChrome_contentbands
0 55.000000 46 0.300 15.0 80 0.750000 20.000000 6.6 1900 54.9 38.5 0.0 0 2.5 0.7 34 40 105.00 100 0
1 62.000000 40 0.433 16.0 80 0.724238 30.000000 6.5 1850 53.8 39.8 0.0 0 2.8 0.9 40 40 103.87 100 0
2 52.000000 40 0.300 16.0 75 0.312000 30.000000 5.6 1467 55.6 38.8 0.0 0 2.5 1.3 40 40 108.06 100 0
3 50.000000 46 0.300 17.0 80 0.750000 30.000000 0.0 2100 57.5 42.5 5.0 0 2.3 0.6 35 40 106.67 100 0
4 50.000000 40 0.267 16.8 76 0.438000 28.000000 8.6 1467 53.8 37.6 5.0 0 2.5 0.8 40 40 103.87 100 0
5 50.000000 46 0.300 16.5 75 0.750000 30.000000 0.0 2600 62.5 37.5 6.0 0 2.5 0.6 30 40 106.67 100 0
6 50.000000 46 0.200 16.5 75 0.750000 28.000000 0.0 2600 62.5 37.5 6.0 0 2.5 1.1 30 40 106.67 100 0
7 50.000000 45 0.367 12.0 70 0.750000 60.000000 0.0 1650 60.2 39.8 1.5 0 3.0 1.0 40 40 103.22 100 1
8 65.000000 43 0.333 16.0 75 1.000000 32.000000 22.7 1750 45.5 31.8 0.0 0 3.0 1.0 38 40 106.66 100 0
9 65.000000 43 0.200 16.0 68 0.750000 30.000000 15.5 1750 48.5 35.9 0.0 0 3.0 1.0 38 40 106.60 100 0
10 50.000000 50 0.367 14.0 80 0.750000 40.000000 10.5 1700 52.6 36.8 0.0 0 2.5 1.0 38 40 105.00 100 1
11 50.000000 50 0.300 15.0 70 1.000000 30.000000 10.0 1600 50.0 40.0 0.0 0 2.8 1.0 38 40 106.66 100 1
12 40.000000 45 0.300 14.5 70 0.625000 25.000000 0.0 1500 59.5 40.5 0.0 0 2.0 1.0 40 40 100.00 100 1
13 50.000000 43 0.267 16.0 75 1.000000 20.000000 15.8 1600 49.5 34.7 0.0 0 2.5 1.0 38 40 103.22 100 0
14 45.015496 45 0.233 15.0 87 1.000000 30.930526 0.0 1400 62.5 37.5 0.0 0 2.5 1.0 40 40 103.22 100 1
15 30.000000 45 0.200 14.0 65 0.724238 30.930526 0.0 1600 62.5 37.5 0.0 0 2.5 0.8 33 40 100.00 100 0
16 30.000000 45 0.200 15.5 65 0.724238 30.930526 5.9 1600 58.8 35.3 0.0 0 2.5 1.8 33 40 100.00 100 0
17 60.000000 38 0.267 16.4 64 0.750000 30.930526 11.0 1400 54.9 34.1 0.0 0 2.5 1.0 32 40 103.22 100 0
18 60.000000 38 0.333 16.5 66 0.750000 30.930526 6.7 1400 56.2 37.1 0.0 0 1.1 1.4 32 40 103.22 100 0
19 50.000000 45 0.233 16.0 70 0.812000 30.000000 0.0 2400 58.8 41.2 0.0 0 1.7 1.0 30 40 107.40 100 0
\n", 1279 | "
" 1280 | ], 1281 | "text/plain": [ 1282 | " Proof_cut Viscosity Caliper Ink_temperature Humifity Roughness \\\n", 1283 | "0 55.000000 46 0.300 15.0 80 0.750000 \n", 1284 | "1 62.000000 40 0.433 16.0 80 0.724238 \n", 1285 | "2 52.000000 40 0.300 16.0 75 0.312000 \n", 1286 | "3 50.000000 46 0.300 17.0 80 0.750000 \n", 1287 | "4 50.000000 40 0.267 16.8 76 0.438000 \n", 1288 | "5 50.000000 46 0.300 16.5 75 0.750000 \n", 1289 | "6 50.000000 46 0.200 16.5 75 0.750000 \n", 1290 | "7 50.000000 45 0.367 12.0 70 0.750000 \n", 1291 | "8 65.000000 43 0.333 16.0 75 1.000000 \n", 1292 | "9 65.000000 43 0.200 16.0 68 0.750000 \n", 1293 | "10 50.000000 50 0.367 14.0 80 0.750000 \n", 1294 | "11 50.000000 50 0.300 15.0 70 1.000000 \n", 1295 | "12 40.000000 45 0.300 14.5 70 0.625000 \n", 1296 | "13 50.000000 43 0.267 16.0 75 1.000000 \n", 1297 | "14 45.015496 45 0.233 15.0 87 1.000000 \n", 1298 | "15 30.000000 45 0.200 14.0 65 0.724238 \n", 1299 | "16 30.000000 45 0.200 15.5 65 0.724238 \n", 1300 | "17 60.000000 38 0.267 16.4 64 0.750000 \n", 1301 | "18 60.000000 38 0.333 16.5 66 0.750000 \n", 1302 | "19 50.000000 45 0.233 16.0 70 0.812000 \n", 1303 | "\n", 1304 | " Blade_pressure Varnish_pct Press_speed Ink_pct Solvent_pct \\\n", 1305 | "0 20.000000 6.6 1900 54.9 38.5 \n", 1306 | "1 30.000000 6.5 1850 53.8 39.8 \n", 1307 | "2 30.000000 5.6 1467 55.6 38.8 \n", 1308 | "3 30.000000 0.0 2100 57.5 42.5 \n", 1309 | "4 28.000000 8.6 1467 53.8 37.6 \n", 1310 | "5 30.000000 0.0 2600 62.5 37.5 \n", 1311 | "6 28.000000 0.0 2600 62.5 37.5 \n", 1312 | "7 60.000000 0.0 1650 60.2 39.8 \n", 1313 | "8 32.000000 22.7 1750 45.5 31.8 \n", 1314 | "9 30.000000 15.5 1750 48.5 35.9 \n", 1315 | "10 40.000000 10.5 1700 52.6 36.8 \n", 1316 | "11 30.000000 10.0 1600 50.0 40.0 \n", 1317 | "12 25.000000 0.0 1500 59.5 40.5 \n", 1318 | "13 20.000000 15.8 1600 49.5 34.7 \n", 1319 | "14 30.930526 0.0 1400 62.5 37.5 \n", 1320 | "15 30.930526 0.0 1600 62.5 37.5 \n", 1321 | "16 30.930526 5.9 1600 58.8 35.3 \n", 1322 | "17 30.930526 11.0 1400 54.9 34.1 \n", 1323 | "18 30.930526 6.7 1400 56.2 37.1 \n", 1324 | "19 30.000000 0.0 2400 58.8 41.2 \n", 1325 | "\n", 1326 | " Esa_voltage ESA_amperage Wax Hardener Roller_durometer Density \\\n", 1327 | "0 0.0 0 2.5 0.7 34 40 \n", 1328 | "1 0.0 0 2.8 0.9 40 40 \n", 1329 | "2 0.0 0 2.5 1.3 40 40 \n", 1330 | "3 5.0 0 2.3 0.6 35 40 \n", 1331 | "4 5.0 0 2.5 0.8 40 40 \n", 1332 | "5 6.0 0 2.5 0.6 30 40 \n", 1333 | "6 6.0 0 2.5 1.1 30 40 \n", 1334 | "7 1.5 0 3.0 1.0 40 40 \n", 1335 | "8 0.0 0 3.0 1.0 38 40 \n", 1336 | "9 0.0 0 3.0 1.0 38 40 \n", 1337 | "10 0.0 0 2.5 1.0 38 40 \n", 1338 | "11 0.0 0 2.8 1.0 38 40 \n", 1339 | "12 0.0 0 2.0 1.0 40 40 \n", 1340 | "13 0.0 0 2.5 1.0 38 40 \n", 1341 | "14 0.0 0 2.5 1.0 40 40 \n", 1342 | "15 0.0 0 2.5 0.8 33 40 \n", 1343 | "16 0.0 0 2.5 1.8 33 40 \n", 1344 | "17 0.0 0 2.5 1.0 32 40 \n", 1345 | "18 0.0 0 1.1 1.4 32 40 \n", 1346 | "19 0.0 0 1.7 1.0 30 40 \n", 1347 | "\n", 1348 | " Anode_ratio Chrome_content bands \n", 1349 | "0 105.00 100 0 \n", 1350 | "1 103.87 100 0 \n", 1351 | "2 108.06 100 0 \n", 1352 | "3 106.67 100 0 \n", 1353 | "4 103.87 100 0 \n", 1354 | "5 106.67 100 0 \n", 1355 | "6 106.67 100 0 \n", 1356 | "7 103.22 100 1 \n", 1357 | "8 106.66 100 0 \n", 1358 | "9 106.60 100 0 \n", 1359 | "10 105.00 100 1 \n", 1360 | "11 106.66 100 1 \n", 1361 | "12 100.00 100 1 \n", 1362 | "13 103.22 100 0 \n", 1363 | "14 103.22 100 1 \n", 1364 | "15 100.00 100 0 \n", 1365 | "16 100.00 100 0 \n", 1366 | "17 103.22 100 0 \n", 1367 | "18 103.22 100 0 \n", 1368 | "19 107.40 100 0 " 1369 | ] 1370 | }, 1371 | "execution_count": 48, 1372 | "metadata": {}, 1373 | "output_type": "execute_result" 1374 | } 1375 | ], 1376 | "source": [ 1377 | "# Mean mode impute\n", 1378 | "imp_df.impute('simple')\n", 1379 | "imp_df.imputed_df().head(20)" 1380 | ] 1381 | }, 1382 | { 1383 | "cell_type": "code", 1384 | "execution_count": 49, 1385 | "metadata": { 1386 | "collapsed": false 1387 | }, 1388 | "outputs": [ 1389 | { 1390 | "name": "stdout", 1391 | "output_type": "stream", 1392 | "text": [ 1393 | "--------------------------------------------------\n", 1394 | "Starting Random Forest Imputation\n", 1395 | "--------------------------------------------------\n", 1396 | "Iteration 1:\n", 1397 | "..........\n", 1398 | "Categorical divergence: 0.000000\n", 1399 | "Continuous divergence: 0.011534\n", 1400 | "Iteration 2:\n", 1401 | "..........\n", 1402 | "Categorical divergence: 0.000000\n", 1403 | "Continuous divergence: 0.002721\n", 1404 | "Iteration 3:\n", 1405 | "..........\n", 1406 | "Categorical divergence: 0.000000\n", 1407 | "Continuous divergence: 0.002200\n", 1408 | "Iteration 4:\n", 1409 | "..........\n", 1410 | "Categorical divergence: 0.000000\n", 1411 | "Continuous divergence: 0.002740\n" 1412 | ] 1413 | }, 1414 | { 1415 | "data": { 1416 | "text/html": [ 1417 | "
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" \n", 2848 | " \n", 2849 | "
Proof_cutViscosityCaliperInk_temperatureHumifityRoughnessBlade_pressureVarnish_pctPress_speedInk_pctSolvent_pctEsa_voltageESA_amperageWaxHardenerRoller_durometerDensityAnode_ratioChrome_contentbands
0 55.000000 46.000000 0.300000 15.00 80 0.750000 20.000000 6.600000 1900 54.900000 38.500000 0.000000 0.000000 2.50 0.700000 34.000000 40 105.000 100.000 0
1 62.000000 40.000000 0.433000 16.00 80 0.570250 30.000000 6.500000 1850 53.800000 39.800000 0.000000 0.000000 2.80 0.900000 40.000000 40 103.870 100.000 0
2 52.000000 40.000000 0.300000 16.00 75 0.312000 30.000000 5.600000 1467 55.600000 38.800000 0.000000 0.000000 2.50 1.300000 40.000000 40 108.060 100.000 0
3 50.000000 46.000000 0.300000 17.00 80 0.750000 30.000000 0.000000 2100 57.500000 42.500000 5.000000 0.000000 2.30 0.600000 35.000000 40 106.670 100.000 0
4 50.000000 40.000000 0.267000 16.80 76 0.438000 28.000000 8.600000 1467 53.800000 37.600000 5.000000 0.000000 2.50 0.800000 40.000000 40 103.870 100.000 0
5 50.000000 46.000000 0.300000 16.50 75 0.750000 30.000000 0.000000 2600 62.500000 37.500000 6.000000 0.000000 2.50 0.600000 30.000000 40 106.670 100.000 0
6 50.000000 46.000000 0.200000 16.50 75 0.750000 28.000000 0.000000 2600 62.500000 37.500000 6.000000 0.000000 2.50 1.100000 30.000000 40 106.670 100.000 0
7 50.000000 45.000000 0.367000 12.00 70 0.750000 60.000000 0.000000 1650 60.200000 39.800000 1.500000 0.000000 3.00 1.000000 40.000000 40 103.220 100.000 1
8 65.000000 43.000000 0.333000 16.00 75 1.000000 32.000000 22.700000 1750 45.500000 31.800000 0.000000 0.000000 3.00 1.000000 38.000000 40 106.660 100.000 0
9 65.000000 43.000000 0.200000 16.00 68 0.750000 30.000000 15.500000 1750 48.500000 35.900000 0.000000 0.000000 3.00 1.000000 38.000000 40 106.600 100.000 0
10 50.000000 50.000000 0.367000 14.00 80 0.750000 40.000000 10.500000 1700 52.600000 36.800000 0.000000 0.000000 2.50 1.000000 38.000000 40 105.000 100.000 1
11 50.000000 50.000000 0.300000 15.00 70 1.000000 30.000000 10.000000 1600 50.000000 40.000000 0.000000 0.000000 2.80 1.000000 38.000000 40 106.660 100.000 1
12 40.000000 45.000000 0.300000 14.50 70 0.625000 25.000000 0.000000 1500 59.500000 40.500000 0.000000 0.000000 2.00 1.000000 40.000000 40 100.000 100.000 1
13 50.000000 43.000000 0.267000 16.00 75 1.000000 20.000000 15.800000 1600 49.500000 34.700000 0.000000 0.000000 2.50 1.000000 38.000000 40 103.220 100.000 0
14 42.071429 45.000000 0.233000 15.00 87 1.000000 29.075645 0.000000 1400 62.500000 37.500000 0.000000 0.000000 2.50 1.000000 40.000000 40 103.220 100.000 1
15 30.000000 45.000000 0.200000 14.00 65 0.770844 36.700282 0.000000 1600 62.500000 37.500000 0.000000 0.000000 2.50 0.800000 33.000000 40 100.000 100.000 0
16 30.000000 45.000000 0.200000 15.50 65 0.834662 32.636215 5.900000 1600 58.800000 35.300000 0.000000 0.000000 2.50 1.800000 33.000000 40 100.000 100.000 0
17 60.000000 38.000000 0.267000 16.40 64 0.750000 28.109982 11.000000 1400 54.900000 34.100000 0.000000 0.000000 2.50 1.000000 32.000000 40 103.220 100.000 0
18 60.000000 38.000000 0.333000 16.50 66 0.750000 34.185481 6.700000 1400 56.200000 37.100000 0.000000 0.000000 1.10 1.400000 32.000000 40 103.220 100.000 0
19 50.000000 45.000000 0.233000 16.00 70 0.812000 30.000000 0.000000 2400 58.800000 41.200000 0.000000 0.000000 1.70 1.000000 30.000000 40 107.400 100.000 0
20 40.000000 45.000000 0.200000 15.00 76 0.812000 30.000000 0.000000 2400 62.500000 37.500000 0.000000 0.000000 1.00 1.000000 30.000000 40 107.400 100.000 0
21 30.000000 43.000000 0.200000 16.30 70 0.812000 25.000000 1.200000 2200 58.100000 40.700000 4.000000 0.000000 2.00 1.000000 30.000000 40 96.875 100.000 0
22 40.000000 41.000000 0.200000 15.80 72 1.000000 30.000000 0.000000 2000 62.500000 37.500000 0.000000 0.000000 2.50 1.000000 30.000000 40 107.400 100.000 1
23 40.000000 42.000000 0.200000 14.50 74 1.000000 25.000000 8.000000 2100 57.500000 34.500000 0.000000 0.000000 2.30 0.700000 35.000000 40 107.400 100.000 1
24 45.000000 42.000000 0.200000 14.00 70 1.000000 20.000000 8.000000 1850 57.500000 34.500000 0.000000 0.000000 2.00 0.700000 35.000000 40 107.400 100.000 1
25 50.000000 45.000000 0.300000 15.00 76 1.000000 30.000000 8.000000 2150 57.500000 34.500000 0.000000 0.000000 2.50 1.000000 35.000000 40 107.400 100.000 0
26 50.000000 45.000000 0.300000 15.20 72 1.000000 25.000000 5.900000 2150 58.800000 35.300000 0.000000 0.000000 2.50 1.000000 35.000000 40 107.400 100.000 0
27 35.000000 42.000000 0.220168 15.00 75 0.750000 30.000000 0.000000 2100 58.800000 41.200000 0.000000 0.000000 2.00 0.800000 30.000000 40 107.400 100.000 1
28 30.000000 45.000000 0.233000 17.00 70 0.750000 35.000000 0.000000 2200 58.800000 41.200000 0.000000 0.000000 2.50 1.000000 33.000000 40 106.450 100.000 1
29 37.500000 44.000000 0.200000 16.00 90 0.750000 28.000000 20.000000 2050 45.000000 35.000000 0.000000 0.000000 2.40 0.800000 35.000000 40 107.400 100.000 0
...............................................................
508 52.969778 56.000000 0.367000 15.10 78 0.500000 29.000000 5.697472 1320 55.684980 38.713045 1.274774 0.019492 2.50 1.297941 36.008094 40 96.900 100.000 1
509 44.598539 47.000000 0.200000 15.00 85 0.500000 24.000000 5.726097 1470 55.650578 38.568891 1.783853 0.033412 1.80 1.700000 33.209189 40 96.900 100.000 1
510 47.081897 55.000000 0.367000 15.50 76 0.750000 30.000000 5.740208 1750 55.664983 38.674870 1.486391 0.013306 2.50 1.200000 34.810744 40 96.900 100.000 1
511 46.368297 53.000000 0.300000 14.20 72 1.000000 28.000000 5.752856 1600 55.649036 38.575393 1.166275 0.029035 2.70 1.500000 37.796955 40 106.100 100.000 1
512 43.814945 39.000000 0.300000 15.10 71 0.625000 30.000000 5.759928 1700 55.648001 38.570943 1.314042 0.035755 2.50 1.500000 36.821588 40 106.100 100.000 1
513 47.164010 46.000000 0.300000 17.00 80 0.250000 25.000000 5.193293 1700 55.644200 39.467991 2.159362 0.014811 2.50 1.500000 35.559200 30 117.700 95.000 1
514 46.314632 35.000000 0.300000 16.20 88 0.500000 28.000000 6.801493 1750 54.578052 38.105582 1.518858 0.024491 2.30 1.000000 35.393599 40 106.000 100.000 1
515 47.781535 40.000000 0.333000 14.50 70 0.750000 60.000000 4.678137 1250 55.356081 39.101730 1.694074 0.304823 2.00 1.500000 38.542304 40 112.500 100.000 1
516 43.984222 39.000000 0.300000 16.00 72 0.750000 50.000000 5.713143 1350 55.654684 38.650257 1.809465 0.043307 2.50 2.200000 34.555786 40 106.600 95.000 1
517 47.394983 40.000000 0.300000 16.50 84 0.625000 26.000000 5.800816 1565 55.648168 38.590815 1.668830 0.036503 2.50 1.000000 36.213413 40 106.600 95.000 1
518 42.010231 44.000000 0.200000 16.22 74 0.750000 30.000000 5.678111 2200 55.643954 38.568989 2.030262 0.026739 2.80 0.800000 32.840508 40 106.600 95.000 1
519 47.410165 47.000000 0.233000 16.50 86 0.250000 30.993024 5.718131 1750 55.659590 38.579452 1.611327 0.037472 2.10 2.100000 34.251112 40 109.600 95.000 1
520 44.861151 52.000000 0.200000 16.00 75 0.312000 25.000000 5.772960 1500 55.655067 38.581793 1.732421 0.046879 2.50 0.600000 35.702393 40 109.100 100.000 1
521 46.524123 56.801242 0.300000 15.00 80 0.500000 30.000000 5.719617 2020 55.638723 38.578615 1.873219 0.034069 2.90 0.500000 34.775970 40 110.300 100.000 1
522 47.977551 54.000000 0.300000 16.50 88 0.750000 25.000000 5.712416 1850 55.647744 38.606364 1.848249 0.029254 3.00 2.000000 34.382930 40 107.140 95.000 1
523 49.486199 56.000000 0.300000 14.00 91 1.125000 27.000000 5.690665 1650 55.615313 38.701198 0.796137 0.026858 3.00 1.500000 37.062472 40 100.000 95.000 1
524 44.286729 50.000000 0.200000 16.50 74 0.625000 34.000000 5.767727 1750 55.648950 38.569232 1.676035 0.035098 2.60 1.000000 35.685359 40 103.125 95.000 1
525 42.463968 56.000000 0.200000 17.00 75 0.750000 25.000000 5.771243 1600 55.648382 38.570793 1.577016 0.039675 2.00 1.000000 34.570304 40 103.125 95.000 1
526 46.829907 55.000000 0.200000 17.00 95 0.625000 27.000000 5.728362 1600 56.843750 40.940000 1.540975 0.032573 2.50 1.000000 36.173583 40 106.250 100.000 1
527 43.569148 54.000000 0.267000 15.50 80 0.750000 40.000000 5.773501 2000 55.653232 38.588915 2.561592 0.014358 1.80 1.300000 33.601579 40 107.140 99.375 1
528 41.551866 54.000000 0.277160 15.50 78 0.812000 30.000000 5.745646 2100 55.655220 38.649378 2.144978 0.005603 1.20 1.000000 31.699908 40 97.050 100.000 1
529 42.760088 53.000000 0.283870 17.00 73 0.750000 35.000000 5.768393 2100 55.649227 38.579400 1.771354 0.011481 2.60 1.300000 32.309152 40 113.300 100.000 1
530 40.531818 54.505741 0.280196 16.00 83 0.662784 28.000000 5.761677 2400 55.649625 38.707885 1.681334 0.018196 2.50 1.000000 31.174148 40 112.500 100.000 1
531 40.218885 60.000000 0.265929 14.90 76 1.000000 30.000000 5.756629 2500 55.644404 38.707282 2.551302 0.017455 0.50 0.500000 31.972358 40 112.500 100.000 1
532 44.040705 56.000000 0.300000 18.30 80 1.000000 32.000000 5.769684 1900 55.647597 38.567872 2.022449 0.019418 1.97 1.126316 35.193635 40 110.000 100.000 1
533 44.785182 52.000000 0.268966 18.00 82 1.000000 25.000000 5.763255 1880 55.647120 38.570866 3.016026 0.007783 1.00 1.000000 33.617790 40 112.500 100.000 1
534 44.037024 50.650000 0.200000 16.10 76 0.500000 34.000000 5.748637 2100 55.669444 38.581040 2.843609 0.068838 0.00 0.000000 32.997040 40 110.000 100.000 1
535 43.008764 49.000000 0.300000 16.50 70 1.000000 34.000000 5.757562 1903 55.647646 38.634071 2.120525 0.033921 2.70 2.800000 33.184724 40 108.000 100.000 1
536 41.473442 46.000000 0.267000 16.40 76 1.000000 34.000000 5.758684 1903 55.647686 38.584046 2.163782 0.015444 1.50 2.300000 32.948129 40 108.000 100.000 1
537 40.727336 46.000000 0.200000 14.20 75 0.750000 25.000000 5.767836 2050 55.645104 38.566884 1.580108 0.037333 2.50 1.000000 33.313200 40 108.100 100.000 1
\n", 2850 | "

538 rows × 20 columns

\n", 2851 | "
" 2852 | ], 2853 | "text/plain": [ 2854 | " Proof_cut Viscosity Caliper Ink_temperature Humifity Roughness \\\n", 2855 | "0 55.000000 46.000000 0.300000 15.00 80 0.750000 \n", 2856 | "1 62.000000 40.000000 0.433000 16.00 80 0.570250 \n", 2857 | "2 52.000000 40.000000 0.300000 16.00 75 0.312000 \n", 2858 | "3 50.000000 46.000000 0.300000 17.00 80 0.750000 \n", 2859 | "4 50.000000 40.000000 0.267000 16.80 76 0.438000 \n", 2860 | "5 50.000000 46.000000 0.300000 16.50 75 0.750000 \n", 2861 | "6 50.000000 46.000000 0.200000 16.50 75 0.750000 \n", 2862 | "7 50.000000 45.000000 0.367000 12.00 70 0.750000 \n", 2863 | "8 65.000000 43.000000 0.333000 16.00 75 1.000000 \n", 2864 | "9 65.000000 43.000000 0.200000 16.00 68 0.750000 \n", 2865 | "10 50.000000 50.000000 0.367000 14.00 80 0.750000 \n", 2866 | "11 50.000000 50.000000 0.300000 15.00 70 1.000000 \n", 2867 | "12 40.000000 45.000000 0.300000 14.50 70 0.625000 \n", 2868 | "13 50.000000 43.000000 0.267000 16.00 75 1.000000 \n", 2869 | "14 42.071429 45.000000 0.233000 15.00 87 1.000000 \n", 2870 | "15 30.000000 45.000000 0.200000 14.00 65 0.770844 \n", 2871 | "16 30.000000 45.000000 0.200000 15.50 65 0.834662 \n", 2872 | "17 60.000000 38.000000 0.267000 16.40 64 0.750000 \n", 2873 | "18 60.000000 38.000000 0.333000 16.50 66 0.750000 \n", 2874 | "19 50.000000 45.000000 0.233000 16.00 70 0.812000 \n", 2875 | "20 40.000000 45.000000 0.200000 15.00 76 0.812000 \n", 2876 | "21 30.000000 43.000000 0.200000 16.30 70 0.812000 \n", 2877 | "22 40.000000 41.000000 0.200000 15.80 72 1.000000 \n", 2878 | "23 40.000000 42.000000 0.200000 14.50 74 1.000000 \n", 2879 | "24 45.000000 42.000000 0.200000 14.00 70 1.000000 \n", 2880 | "25 50.000000 45.000000 0.300000 15.00 76 1.000000 \n", 2881 | "26 50.000000 45.000000 0.300000 15.20 72 1.000000 \n", 2882 | "27 35.000000 42.000000 0.220168 15.00 75 0.750000 \n", 2883 | "28 30.000000 45.000000 0.233000 17.00 70 0.750000 \n", 2884 | "29 37.500000 44.000000 0.200000 16.00 90 0.750000 \n", 2885 | ".. ... ... ... ... ... ... \n", 2886 | "508 52.969778 56.000000 0.367000 15.10 78 0.500000 \n", 2887 | "509 44.598539 47.000000 0.200000 15.00 85 0.500000 \n", 2888 | "510 47.081897 55.000000 0.367000 15.50 76 0.750000 \n", 2889 | "511 46.368297 53.000000 0.300000 14.20 72 1.000000 \n", 2890 | "512 43.814945 39.000000 0.300000 15.10 71 0.625000 \n", 2891 | "513 47.164010 46.000000 0.300000 17.00 80 0.250000 \n", 2892 | "514 46.314632 35.000000 0.300000 16.20 88 0.500000 \n", 2893 | "515 47.781535 40.000000 0.333000 14.50 70 0.750000 \n", 2894 | "516 43.984222 39.000000 0.300000 16.00 72 0.750000 \n", 2895 | "517 47.394983 40.000000 0.300000 16.50 84 0.625000 \n", 2896 | "518 42.010231 44.000000 0.200000 16.22 74 0.750000 \n", 2897 | "519 47.410165 47.000000 0.233000 16.50 86 0.250000 \n", 2898 | "520 44.861151 52.000000 0.200000 16.00 75 0.312000 \n", 2899 | "521 46.524123 56.801242 0.300000 15.00 80 0.500000 \n", 2900 | "522 47.977551 54.000000 0.300000 16.50 88 0.750000 \n", 2901 | "523 49.486199 56.000000 0.300000 14.00 91 1.125000 \n", 2902 | "524 44.286729 50.000000 0.200000 16.50 74 0.625000 \n", 2903 | "525 42.463968 56.000000 0.200000 17.00 75 0.750000 \n", 2904 | "526 46.829907 55.000000 0.200000 17.00 95 0.625000 \n", 2905 | "527 43.569148 54.000000 0.267000 15.50 80 0.750000 \n", 2906 | "528 41.551866 54.000000 0.277160 15.50 78 0.812000 \n", 2907 | "529 42.760088 53.000000 0.283870 17.00 73 0.750000 \n", 2908 | "530 40.531818 54.505741 0.280196 16.00 83 0.662784 \n", 2909 | "531 40.218885 60.000000 0.265929 14.90 76 1.000000 \n", 2910 | "532 44.040705 56.000000 0.300000 18.30 80 1.000000 \n", 2911 | "533 44.785182 52.000000 0.268966 18.00 82 1.000000 \n", 2912 | "534 44.037024 50.650000 0.200000 16.10 76 0.500000 \n", 2913 | "535 43.008764 49.000000 0.300000 16.50 70 1.000000 \n", 2914 | "536 41.473442 46.000000 0.267000 16.40 76 1.000000 \n", 2915 | "537 40.727336 46.000000 0.200000 14.20 75 0.750000 \n", 2916 | "\n", 2917 | " Blade_pressure Varnish_pct Press_speed Ink_pct Solvent_pct \\\n", 2918 | "0 20.000000 6.600000 1900 54.900000 38.500000 \n", 2919 | "1 30.000000 6.500000 1850 53.800000 39.800000 \n", 2920 | "2 30.000000 5.600000 1467 55.600000 38.800000 \n", 2921 | "3 30.000000 0.000000 2100 57.500000 42.500000 \n", 2922 | "4 28.000000 8.600000 1467 53.800000 37.600000 \n", 2923 | "5 30.000000 0.000000 2600 62.500000 37.500000 \n", 2924 | "6 28.000000 0.000000 2600 62.500000 37.500000 \n", 2925 | "7 60.000000 0.000000 1650 60.200000 39.800000 \n", 2926 | "8 32.000000 22.700000 1750 45.500000 31.800000 \n", 2927 | "9 30.000000 15.500000 1750 48.500000 35.900000 \n", 2928 | "10 40.000000 10.500000 1700 52.600000 36.800000 \n", 2929 | "11 30.000000 10.000000 1600 50.000000 40.000000 \n", 2930 | "12 25.000000 0.000000 1500 59.500000 40.500000 \n", 2931 | "13 20.000000 15.800000 1600 49.500000 34.700000 \n", 2932 | "14 29.075645 0.000000 1400 62.500000 37.500000 \n", 2933 | "15 36.700282 0.000000 1600 62.500000 37.500000 \n", 2934 | "16 32.636215 5.900000 1600 58.800000 35.300000 \n", 2935 | "17 28.109982 11.000000 1400 54.900000 34.100000 \n", 2936 | "18 34.185481 6.700000 1400 56.200000 37.100000 \n", 2937 | "19 30.000000 0.000000 2400 58.800000 41.200000 \n", 2938 | "20 30.000000 0.000000 2400 62.500000 37.500000 \n", 2939 | "21 25.000000 1.200000 2200 58.100000 40.700000 \n", 2940 | "22 30.000000 0.000000 2000 62.500000 37.500000 \n", 2941 | "23 25.000000 8.000000 2100 57.500000 34.500000 \n", 2942 | "24 20.000000 8.000000 1850 57.500000 34.500000 \n", 2943 | "25 30.000000 8.000000 2150 57.500000 34.500000 \n", 2944 | "26 25.000000 5.900000 2150 58.800000 35.300000 \n", 2945 | "27 30.000000 0.000000 2100 58.800000 41.200000 \n", 2946 | "28 35.000000 0.000000 2200 58.800000 41.200000 \n", 2947 | "29 28.000000 20.000000 2050 45.000000 35.000000 \n", 2948 | ".. ... ... ... ... ... \n", 2949 | "508 29.000000 5.697472 1320 55.684980 38.713045 \n", 2950 | "509 24.000000 5.726097 1470 55.650578 38.568891 \n", 2951 | "510 30.000000 5.740208 1750 55.664983 38.674870 \n", 2952 | "511 28.000000 5.752856 1600 55.649036 38.575393 \n", 2953 | "512 30.000000 5.759928 1700 55.648001 38.570943 \n", 2954 | "513 25.000000 5.193293 1700 55.644200 39.467991 \n", 2955 | "514 28.000000 6.801493 1750 54.578052 38.105582 \n", 2956 | "515 60.000000 4.678137 1250 55.356081 39.101730 \n", 2957 | "516 50.000000 5.713143 1350 55.654684 38.650257 \n", 2958 | "517 26.000000 5.800816 1565 55.648168 38.590815 \n", 2959 | "518 30.000000 5.678111 2200 55.643954 38.568989 \n", 2960 | "519 30.993024 5.718131 1750 55.659590 38.579452 \n", 2961 | "520 25.000000 5.772960 1500 55.655067 38.581793 \n", 2962 | "521 30.000000 5.719617 2020 55.638723 38.578615 \n", 2963 | "522 25.000000 5.712416 1850 55.647744 38.606364 \n", 2964 | "523 27.000000 5.690665 1650 55.615313 38.701198 \n", 2965 | "524 34.000000 5.767727 1750 55.648950 38.569232 \n", 2966 | "525 25.000000 5.771243 1600 55.648382 38.570793 \n", 2967 | "526 27.000000 5.728362 1600 56.843750 40.940000 \n", 2968 | "527 40.000000 5.773501 2000 55.653232 38.588915 \n", 2969 | "528 30.000000 5.745646 2100 55.655220 38.649378 \n", 2970 | "529 35.000000 5.768393 2100 55.649227 38.579400 \n", 2971 | "530 28.000000 5.761677 2400 55.649625 38.707885 \n", 2972 | "531 30.000000 5.756629 2500 55.644404 38.707282 \n", 2973 | "532 32.000000 5.769684 1900 55.647597 38.567872 \n", 2974 | "533 25.000000 5.763255 1880 55.647120 38.570866 \n", 2975 | "534 34.000000 5.748637 2100 55.669444 38.581040 \n", 2976 | "535 34.000000 5.757562 1903 55.647646 38.634071 \n", 2977 | "536 34.000000 5.758684 1903 55.647686 38.584046 \n", 2978 | "537 25.000000 5.767836 2050 55.645104 38.566884 \n", 2979 | "\n", 2980 | " Esa_voltage ESA_amperage Wax Hardener Roller_durometer Density \\\n", 2981 | "0 0.000000 0.000000 2.50 0.700000 34.000000 40 \n", 2982 | "1 0.000000 0.000000 2.80 0.900000 40.000000 40 \n", 2983 | "2 0.000000 0.000000 2.50 1.300000 40.000000 40 \n", 2984 | "3 5.000000 0.000000 2.30 0.600000 35.000000 40 \n", 2985 | "4 5.000000 0.000000 2.50 0.800000 40.000000 40 \n", 2986 | "5 6.000000 0.000000 2.50 0.600000 30.000000 40 \n", 2987 | "6 6.000000 0.000000 2.50 1.100000 30.000000 40 \n", 2988 | "7 1.500000 0.000000 3.00 1.000000 40.000000 40 \n", 2989 | "8 0.000000 0.000000 3.00 1.000000 38.000000 40 \n", 2990 | "9 0.000000 0.000000 3.00 1.000000 38.000000 40 \n", 2991 | "10 0.000000 0.000000 2.50 1.000000 38.000000 40 \n", 2992 | "11 0.000000 0.000000 2.80 1.000000 38.000000 40 \n", 2993 | "12 0.000000 0.000000 2.00 1.000000 40.000000 40 \n", 2994 | "13 0.000000 0.000000 2.50 1.000000 38.000000 40 \n", 2995 | "14 0.000000 0.000000 2.50 1.000000 40.000000 40 \n", 2996 | "15 0.000000 0.000000 2.50 0.800000 33.000000 40 \n", 2997 | "16 0.000000 0.000000 2.50 1.800000 33.000000 40 \n", 2998 | "17 0.000000 0.000000 2.50 1.000000 32.000000 40 \n", 2999 | "18 0.000000 0.000000 1.10 1.400000 32.000000 40 \n", 3000 | "19 0.000000 0.000000 1.70 1.000000 30.000000 40 \n", 3001 | "20 0.000000 0.000000 1.00 1.000000 30.000000 40 \n", 3002 | "21 4.000000 0.000000 2.00 1.000000 30.000000 40 \n", 3003 | "22 0.000000 0.000000 2.50 1.000000 30.000000 40 \n", 3004 | "23 0.000000 0.000000 2.30 0.700000 35.000000 40 \n", 3005 | "24 0.000000 0.000000 2.00 0.700000 35.000000 40 \n", 3006 | "25 0.000000 0.000000 2.50 1.000000 35.000000 40 \n", 3007 | "26 0.000000 0.000000 2.50 1.000000 35.000000 40 \n", 3008 | "27 0.000000 0.000000 2.00 0.800000 30.000000 40 \n", 3009 | "28 0.000000 0.000000 2.50 1.000000 33.000000 40 \n", 3010 | "29 0.000000 0.000000 2.40 0.800000 35.000000 40 \n", 3011 | ".. ... ... ... ... ... ... \n", 3012 | "508 1.274774 0.019492 2.50 1.297941 36.008094 40 \n", 3013 | "509 1.783853 0.033412 1.80 1.700000 33.209189 40 \n", 3014 | "510 1.486391 0.013306 2.50 1.200000 34.810744 40 \n", 3015 | "511 1.166275 0.029035 2.70 1.500000 37.796955 40 \n", 3016 | "512 1.314042 0.035755 2.50 1.500000 36.821588 40 \n", 3017 | "513 2.159362 0.014811 2.50 1.500000 35.559200 30 \n", 3018 | "514 1.518858 0.024491 2.30 1.000000 35.393599 40 \n", 3019 | "515 1.694074 0.304823 2.00 1.500000 38.542304 40 \n", 3020 | "516 1.809465 0.043307 2.50 2.200000 34.555786 40 \n", 3021 | "517 1.668830 0.036503 2.50 1.000000 36.213413 40 \n", 3022 | "518 2.030262 0.026739 2.80 0.800000 32.840508 40 \n", 3023 | "519 1.611327 0.037472 2.10 2.100000 34.251112 40 \n", 3024 | "520 1.732421 0.046879 2.50 0.600000 35.702393 40 \n", 3025 | "521 1.873219 0.034069 2.90 0.500000 34.775970 40 \n", 3026 | "522 1.848249 0.029254 3.00 2.000000 34.382930 40 \n", 3027 | "523 0.796137 0.026858 3.00 1.500000 37.062472 40 \n", 3028 | "524 1.676035 0.035098 2.60 1.000000 35.685359 40 \n", 3029 | "525 1.577016 0.039675 2.00 1.000000 34.570304 40 \n", 3030 | "526 1.540975 0.032573 2.50 1.000000 36.173583 40 \n", 3031 | "527 2.561592 0.014358 1.80 1.300000 33.601579 40 \n", 3032 | "528 2.144978 0.005603 1.20 1.000000 31.699908 40 \n", 3033 | "529 1.771354 0.011481 2.60 1.300000 32.309152 40 \n", 3034 | "530 1.681334 0.018196 2.50 1.000000 31.174148 40 \n", 3035 | "531 2.551302 0.017455 0.50 0.500000 31.972358 40 \n", 3036 | "532 2.022449 0.019418 1.97 1.126316 35.193635 40 \n", 3037 | "533 3.016026 0.007783 1.00 1.000000 33.617790 40 \n", 3038 | "534 2.843609 0.068838 0.00 0.000000 32.997040 40 \n", 3039 | "535 2.120525 0.033921 2.70 2.800000 33.184724 40 \n", 3040 | "536 2.163782 0.015444 1.50 2.300000 32.948129 40 \n", 3041 | "537 1.580108 0.037333 2.50 1.000000 33.313200 40 \n", 3042 | "\n", 3043 | " Anode_ratio Chrome_content bands \n", 3044 | "0 105.000 100.000 0 \n", 3045 | "1 103.870 100.000 0 \n", 3046 | "2 108.060 100.000 0 \n", 3047 | "3 106.670 100.000 0 \n", 3048 | "4 103.870 100.000 0 \n", 3049 | "5 106.670 100.000 0 \n", 3050 | "6 106.670 100.000 0 \n", 3051 | "7 103.220 100.000 1 \n", 3052 | "8 106.660 100.000 0 \n", 3053 | "9 106.600 100.000 0 \n", 3054 | "10 105.000 100.000 1 \n", 3055 | "11 106.660 100.000 1 \n", 3056 | "12 100.000 100.000 1 \n", 3057 | "13 103.220 100.000 0 \n", 3058 | "14 103.220 100.000 1 \n", 3059 | "15 100.000 100.000 0 \n", 3060 | "16 100.000 100.000 0 \n", 3061 | "17 103.220 100.000 0 \n", 3062 | "18 103.220 100.000 0 \n", 3063 | "19 107.400 100.000 0 \n", 3064 | "20 107.400 100.000 0 \n", 3065 | "21 96.875 100.000 0 \n", 3066 | "22 107.400 100.000 1 \n", 3067 | "23 107.400 100.000 1 \n", 3068 | "24 107.400 100.000 1 \n", 3069 | "25 107.400 100.000 0 \n", 3070 | "26 107.400 100.000 0 \n", 3071 | "27 107.400 100.000 1 \n", 3072 | "28 106.450 100.000 1 \n", 3073 | "29 107.400 100.000 0 \n", 3074 | ".. ... ... ... \n", 3075 | "508 96.900 100.000 1 \n", 3076 | "509 96.900 100.000 1 \n", 3077 | "510 96.900 100.000 1 \n", 3078 | "511 106.100 100.000 1 \n", 3079 | "512 106.100 100.000 1 \n", 3080 | "513 117.700 95.000 1 \n", 3081 | "514 106.000 100.000 1 \n", 3082 | "515 112.500 100.000 1 \n", 3083 | "516 106.600 95.000 1 \n", 3084 | "517 106.600 95.000 1 \n", 3085 | "518 106.600 95.000 1 \n", 3086 | "519 109.600 95.000 1 \n", 3087 | "520 109.100 100.000 1 \n", 3088 | "521 110.300 100.000 1 \n", 3089 | "522 107.140 95.000 1 \n", 3090 | "523 100.000 95.000 1 \n", 3091 | "524 103.125 95.000 1 \n", 3092 | "525 103.125 95.000 1 \n", 3093 | "526 106.250 100.000 1 \n", 3094 | "527 107.140 99.375 1 \n", 3095 | "528 97.050 100.000 1 \n", 3096 | "529 113.300 100.000 1 \n", 3097 | "530 112.500 100.000 1 \n", 3098 | "531 112.500 100.000 1 \n", 3099 | "532 110.000 100.000 1 \n", 3100 | "533 112.500 100.000 1 \n", 3101 | "534 110.000 100.000 1 \n", 3102 | "535 108.000 100.000 1 \n", 3103 | "536 108.000 100.000 1 \n", 3104 | "537 108.100 100.000 1 \n", 3105 | "\n", 3106 | "[538 rows x 20 columns]" 3107 | ] 3108 | }, 3109 | "execution_count": 49, 3110 | "metadata": {}, 3111 | "output_type": "execute_result" 3112 | } 3113 | ], 3114 | "source": [ 3115 | "# Random Forest Impute\n", 3116 | "imp_df.impute('random_forest', {'n_estimators': 50, 'n_jobs': 4})\n", 3117 | "imp_df.imputed_df()" 3118 | ] 3119 | }, 3120 | { 3121 | "cell_type": "code", 3122 | "execution_count": 50, 3123 | "metadata": { 3124 | "collapsed": false 3125 | }, 3126 | "outputs": [ 3127 | { 3128 | "data": { 3129 | "text/plain": [ 3130 | "{'Anode_ratio': 0.17134411375781733,\n", 3131 | " 'Blade_pressure': 0.34666681500405583,\n", 3132 | " 'Caliper': 0.15279301047315719,\n", 3133 | " 'Chrome_content': 0.28642410154192433,\n", 3134 | " 'Density': 0.1839626490143208,\n", 3135 | " 'ESA_amperage': -0.10019459183802693,\n", 3136 | " 'Esa_voltage': 0.03848497910315507,\n", 3137 | " 'Hardener': 0.15191973694764827,\n", 3138 | " 'Humifity': 0.12277197977333476,\n", 3139 | " 'Ink_pct': 0.94165167046077214,\n", 3140 | " 'Ink_temperature': 0.018347262893288474,\n", 3141 | " 'Press_speed': 0.48675156249285967,\n", 3142 | " 'Proof_cut': 0.31275595447672344,\n", 3143 | " 'Roller_durometer': 0.34921211376020633,\n", 3144 | " 'Roughness': 0.026598048725949175,\n", 3145 | " 'Solvent_pct': 0.76799293975064564,\n", 3146 | " 'Varnish_pct': 0.95257836078050162,\n", 3147 | " 'Viscosity': 0.1119707739208341,\n", 3148 | " 'Wax': 0.40225880586737284,\n", 3149 | " 'bands': 0.76394052044609662}" 3150 | ] 3151 | }, 3152 | "execution_count": 50, 3153 | "metadata": {}, 3154 | "output_type": "execute_result" 3155 | } 3156 | ], 3157 | "source": [ 3158 | "imp_df.imputation_scores" 3159 | ] 3160 | } 3161 | ], 3162 | "metadata": { 3163 | "kernelspec": { 3164 | "display_name": "Python 2", 3165 | "language": "python", 3166 | "name": "python2" 3167 | }, 3168 | "language_info": { 3169 | "codemirror_mode": { 3170 | "name": "ipython", 3171 | "version": 2 3172 | }, 3173 | "file_extension": ".py", 3174 | "mimetype": "text/x-python", 3175 | "name": "python", 3176 | "nbconvert_exporter": "python", 3177 | "pygments_lexer": "ipython2", 3178 | "version": "2.7.10" 3179 | } 3180 | }, 3181 | "nbformat": 4, 3182 | "nbformat_minor": 0 3183 | } 3184 | --------------------------------------------------------------------------------