Why hyperopt is giving the best loss Nan while operating in Random Forest?

Question

I am solving a Kaggle Problem: https://www.kaggle.com/c/forest-cover-type-prediction/data I used hyperopt to find optimal hyperparameter for Random Forest. But I am stuck here, as for almost most of the iteration it is giving best loss: Nan.

My Full Code :

import pandas as pd
import numpy as np

# Lets Load the Dataset

train = pd.read_csv(r"D:\Study Material\Py_Programs\Data Sets\forest-cover-type-prediction\train.csv")
test =  pd.read_csv(r"D:\Study Material\Py_Programs\Data Sets\forest-cover-type-prediction\test.csv")

# Lets Append all together so that we can study altogether    
# Lets not include test_2 for a while

test['Cover_Type'] = np.nan
data = train.append(test,ignore_index = True)
del train,test

# Lets Now do Feature Engineering
# We could do Manula Feature Engineering but lets not do it 
# Lets use feature Tools
# Lets first create a simple new attribute that can later be index for Soil Enity

data['Id_Soil'] = np.arange(len(data))

import featuretools as ft
es = ft.EntitySet(id = 'forest')
es = es.entity_from_dataframe(entity_id = 'Forest_Pred',dataframe = data,index = 'Id')

>>> es
Entityset: forest
  Entities:
    Forest_Pred [Rows: 581012, Columns: 63]
  Relationships:
    No relationships

# Lets Make a Seperate Entity for Soil
Additional_Variable = data.columns[data.columns.str.startswith('Soil')]
Additional_Variable
es = es.normalize_entity(base_entity_id = 'Forest_Pred',new_entity_id = 'Soil',index = 'Id_Soil',additional_variables = 
                        list(Additional_Variable))

>>>es 
Entityset: forest
  Entities:
    Forest_Pred [Rows: 581012, Columns: 23]
    Soil [Rows: 581012, Columns: 41]
  Relationships:
    Forest_Pred.Id_Soil -> Soil.Id_Soil

# Lets Run DFS 
feature_matrix,feature_defs = ft.dfs(entityset = es,target_entity = 'Forest_Pred')

drop_cols = []
for col in feature_matrix:
    if col == 'Cover_Type':
        pass
    else:
        if 'Cover_Type' in col:
            drop_cols.append(col)

feature_matrix = feature_matrix[[x for x in feature_matrix if x not in drop_cols]]         
feature_matrix.head()


# Create correlation matrix
corr_matrix = feature_matrix.corr().abs()
# Select upper triangle of correlation matrix
upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))
# Find index of feature columns with correlation greater than 0.95
to_drop = [column for column in upper.columns if any(upper[column] >= 0.95)]

print('There are {} columns with >= 0.95 correlation.'.format(len(to_drop)))

>>>to_drop

There are 83 columns with >= 0.95 correlation.

# These are the Redundant Columns

['New2',      # I manually created New2, Hill1 and Hill3
 'Hill1',
 'Hill3',
 'Soil.SUM(Forest_Pred.wild1)',
 'Soil.SUM(Forest_Pred.Vertical_Distance_To_Hydrology)',
 'Soil.SUM(Forest_Pred.Hillshade_Noon)',
 'Soil.SUM(Forest_Pred.Horizontal_Distance_To_Roadways)',
 'Soil.SUM(Forest_Pred.Slope)',
 'Soil.SUM(Forest_Pred.Wilderness_Area4)',
 'Soil.SUM(Forest_Pred.New4)',
 'Soil.SUM(Forest_Pred.Hill2)',
 'Soil.SUM(Forest_Pred.New2)',
 'Soil.SUM(Forest_Pred.Wilderness_Area2)',
 'Soil.SUM(Forest_Pred.Horizontal_Distance_To_Hydrology)',
 'Soil.SUM(Forest_Pred.Hillshade_9am)',
 'Soil.SUM(Forest_Pred.Aspect)',
 'Soil.SUM(Forest_Pred.Hillshade_3pm)',
 'Soil.SUM(Forest_Pred.Hill1)',
 'Soil.SUM(Forest_Pred.Hill3)',
 'Soil.SUM(Forest_Pred.Elevation)',
 'Soil.SUM(Forest_Pred.Wilderness_Area3)',
 'Soil.SUM(Forest_Pred.Horizontal_Distance_To_Fire_Points)',
 'Soil.SUM(Forest_Pred.Wilderness_Area1)',
 'Soil.MAX(Forest_Pred.wild1)',
 'Soil.MAX(Forest_Pred.Vertical_Distance_To_Hydrology)',
 'Soil.MAX(Forest_Pred.Hillshade_Noon)',
 'Soil.MAX(Forest_Pred.Horizontal_Distance_To_Roadways)',
 'Soil.MAX(Forest_Pred.Slope)',
 'Soil.MAX(Forest_Pred.Wilderness_Area4)',
 'Soil.MAX(Forest_Pred.New4)',
 'Soil.MAX(Forest_Pred.Hill2)',
 'Soil.MAX(Forest_Pred.New2)',
 'Soil.MAX(Forest_Pred.Wilderness_Area2)',
 'Soil.MAX(Forest_Pred.Horizontal_Distance_To_Hydrology)',
 'Soil.MAX(Forest_Pred.Hillshade_9am)',
 'Soil.MAX(Forest_Pred.Aspect)',
 'Soil.MAX(Forest_Pred.Hillshade_3pm)',
 'Soil.MAX(Forest_Pred.Hill1)',
 'Soil.MAX(Forest_Pred.Hill3)',
 'Soil.MAX(Forest_Pred.Elevation)',
 'Soil.MAX(Forest_Pred.Wilderness_Area3)',
 'Soil.MAX(Forest_Pred.Horizontal_Distance_To_Fire_Points)',
 'Soil.MAX(Forest_Pred.Wilderness_Area1)',
 'Soil.MIN(Forest_Pred.wild1)',
 'Soil.MIN(Forest_Pred.Vertical_Distance_To_Hydrology)',
 'Soil.MIN(Forest_Pred.Hillshade_Noon)',
 'Soil.MIN(Forest_Pred.Horizontal_Distance_To_Roadways)',
 'Soil.MIN(Forest_Pred.Slope)',
 'Soil.MIN(Forest_Pred.Wilderness_Area4)',
 'Soil.MIN(Forest_Pred.New4)',
 'Soil.MIN(Forest_Pred.Hill2)',
 'Soil.MIN(Forest_Pred.New2)',
 'Soil.MIN(Forest_Pred.Wilderness_Area2)',
 'Soil.MIN(Forest_Pred.Horizontal_Distance_To_Hydrology)',
 'Soil.MIN(Forest_Pred.Hillshade_9am)',
 'Soil.MIN(Forest_Pred.Aspect)',
 'Soil.MIN(Forest_Pred.Hillshade_3pm)',
 'Soil.MIN(Forest_Pred.Hill1)',
 'Soil.MIN(Forest_Pred.Hill3)',
 'Soil.MIN(Forest_Pred.Elevation)',
 'Soil.MIN(Forest_Pred.Wilderness_Area3)',
 'Soil.MIN(Forest_Pred.Horizontal_Distance_To_Fire_Points)',
 'Soil.MIN(Forest_Pred.Wilderness_Area1)',
 'Soil.MEAN(Forest_Pred.wild1)',
 'Soil.MEAN(Forest_Pred.Vertical_Distance_To_Hydrology)',
 'Soil.MEAN(Forest_Pred.Hillshade_Noon)',
 'Soil.MEAN(Forest_Pred.Horizontal_Distance_To_Roadways)',
 'Soil.MEAN(Forest_Pred.Slope)',
 'Soil.MEAN(Forest_Pred.Wilderness_Area4)',
 'Soil.MEAN(Forest_Pred.New4)',
 'Soil.MEAN(Forest_Pred.Hill2)',
 'Soil.MEAN(Forest_Pred.New2)',
 'Soil.MEAN(Forest_Pred.Wilderness_Area2)',
 'Soil.MEAN(Forest_Pred.Horizontal_Distance_To_Hydrology)',
 'Soil.MEAN(Forest_Pred.Hillshade_9am)',
 'Soil.MEAN(Forest_Pred.Aspect)',
 'Soil.MEAN(Forest_Pred.Hillshade_3pm)',
 'Soil.MEAN(Forest_Pred.Hill1)',
 'Soil.MEAN(Forest_Pred.Hill3)',
 'Soil.MEAN(Forest_Pred.Elevation)',
 'Soil.MEAN(Forest_Pred.Wilderness_Area3)',
 'Soil.MEAN(Forest_Pred.Horizontal_Distance_To_Fire_Points)',
 'Soil.MEAN(Forest_Pred.Wilderness_Area1)']


# Lets get the feature first
# Lets Now Look at the NULL Values

Null_Values = pd.DataFrame(train.isnull().sum()).rename(columns = {0 : 'Total'})
Null_Values['Percentage'] = Null_Values['Total']/len(train)
Null_Values.sort_values('Percentage',ascending = False)
Fully_Null_Columns = Null_Values.loc[Null_Values['Percentage'] == 1.0]
To_Remove = Fully_Null_Columns.index


Feature = list(train.columns)
for Val in To_Remove:
    Feature.remove(Val)

>>>len(Feature)
58

Pipe = Pipeline([
    ('impute',Imputer(strategy = 'median')),
    ('scaler',MinMaxScaler())
])
train = Pipe.fit_transform(train)
test = Pipe.transform(test)


######################## Hyperopt Part Begins From Here ###############################333


# Lets Apply Hyperopt to Optimize the Two Model that we think may do good Random Forest and MLP
# lETS fIRST dO For Random Forest
#Lets Define The Objective Function for it

from hyperopt import STATUS_OK

def Objective_Forest(params):
    classifier = RandomForestClassifier(**params)
    score = cross_val_score(classifier,train,Target,cv = 10,scoring = scorer)
    Best_Score = 1 - np.mean(score)
    return {'loss': Best_Score,'params':params,'status':STATUS_OK}

# lETS DEFINE THE PARAMETER SPACE FOR THE RANDOM FOREST CLASSIFIER
from hyperopt import hp
Param_grid = {
    'n_estimators': hp.choice('n_estimators',range(10,1000)),
    'max_depth' : hp.choice('max_depth',range(1,20)),
    'min_samples_split': hp.choice('min_samples_split',range(2,20)),
    'min_samples_leaf': hp.choice('min_samples_leaf',range(1,11)),
    'min_weight_fraction_leaf': hp.uniform('min_weight_fraction_leaf',0.0,1.0),
    'max_features':hp.choice('max_features',["sqrt", "log2","None",0.2,0.5,0.8]),
    'max_leaf_nodes':hp.choice('max_leaf_nodes',range(10,150)),
    'min_impurity_decrease':hp.uniform('min_impurity_decrease',0.0,1.0),
    'class_weight':hp.choice('class_weight',[None,'balanced']),
    'max_samples':hp.uniform('max_samples',0.0,1.0)
}

from hyperopt import tpe
tpe_algo = tpe.suggest


from hyperopt import Trials
bayes_trials = Trials()


from hyperopt import fmin
MAX_EVALS = 100
# Optimize
best = fmin(fn = Objective_Forest,space = Param_grid,algo = tpe_algo,max_evals = MAX_EVALS,trials = bayes_trials)

>>> [print(t['result'],end = '\n\n\n') for t in bayes_trials.trials]

{'loss': nan, 'params': {'class_weight': 'balanced', 'max_depth': 1, 'max_features': 'None', 'max_leaf_nodes': 33, 'max_samples': 0.4660771469206677, 'min_impurity_decrease': 0.45511437833393464, 'min_samples_leaf': 10, 'min_samples_split': 8, 'min_weight_fraction_leaf': 0.9339453161850745, 'n_estimators': 972}, 'status': 'ok'}

{'loss': nan, 'params': {'class_weight': None, 'max_depth': 11, 'max_features': 'log2', 'max_leaf_nodes': 49, 'max_samples': 0.14947278280397347, 'min_impurity_decrease': 0.2358674422658822, 'min_samples_leaf': 9, 'min_samples_split': 16, 'min_weight_fraction_leaf': 0.5935700756502073, 'n_estimators': 436}, 'status': 'ok'}


{'loss': nan, 'params': {'class_weight': None, 'max_depth': 16, 'max_features': 'None', 'max_leaf_nodes': 64, 'max_samples': 0.008126252217055763, 'min_impurity_decrease': 0.5860665211910298, 'min_samples_leaf': 3, 'min_samples_split': 14, 'min_weight_fraction_leaf': 0.7589621329866701, 'n_estimators': 544}, 'status': 'ok'}


{'loss': nan, 'params': {'class_weight': None, 'max_depth': 13, 'max_features': 'None', 'max_leaf_nodes': 88, 'max_samples': 0.8342507642254701, 'min_impurity_decrease': 0.29169826447891134, 'min_samples_leaf': 9, 'min_samples_split': 14, 'min_weight_fraction_leaf': 0.5732868446872494, 'n_estimators': 759}, 'status': 'ok'}


{'loss': 0.514714207538852, 'params': {'class_weight': None, 'max_depth': 4, 'max_features': 'sqrt', 'max_leaf_nodes': 104, 'max_samples': 0.10435155448150135, 'min_impurity_decrease': 0.024801820935633656, 'min_samples_leaf': 5, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.09350127980207612, 'n_estimators': 739}, 'status': 'ok'}


{'loss': 0.9642857142857143, 'params': {'class_weight': 'balanced', 'max_depth': 5, 'max_features': 'log2', 'max_leaf_nodes': 86, 'max_samples': 0.029032222646389272, 'min_impurity_decrease': 0.4459819146508117, 'min_samples_leaf': 5, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.16673304793166255, 'n_estimators': 419}, 'status': 'ok'}


{'loss': nan, 'params': {'class_weight': 'balanced', 'max_depth': 1, 'max_features': 'None', 'max_leaf_nodes': 18, 'max_samples': 0.4913763122828826, 'min_impurity_decrease': 0.35382231135300235, 'min_samples_leaf': 3, 'min_samples_split': 18, 'min_weight_fraction_leaf': 0.7421569901774066, 'n_estimators': 354}, 'status': 'ok'}


{'loss': nan, 'params': {'class_weight': 'balanced', 'max_depth': 4, 'max_features': 'sqrt', 'max_leaf_nodes': 69, 'max_samples': 0.27201985914939086, 'min_impurity_decrease': 0.486936153640398, 'min_samples_leaf': 8, 'min_samples_split': 15, 'min_weight_fraction_leaf': 0.7310520866089266, 'n_estimators': 142}, 'status': 'ok'}


{'loss': nan, 'params': {'class_weight': None, 'max_depth': 12, 'max_features': 'sqrt', 'max_leaf_nodes': 36, 'max_samples': 0.9771715541709761, 'min_impurity_decrease': 0.1971412468087903, 'min_samples_leaf': 9, 'min_samples_split': 3, 'min_weight_fraction_leaf': 0.8200016570398415, 'n_estimators': 34}, 'status': 'ok'}


{'loss': 0.9642857142857143, 'params': {'class_weight': None, 'max_depth': 10, 'max_features': 'sqrt', 'max_leaf_nodes': 73, 'max_samples': 0.45641569744506405, 'min_impurity_decrease': 0.8403030256419523, 'min_samples_leaf': 7, 'min_samples_split': 9, 'min_weight_fraction_leaf': 0.0701815156303528, 'n_estimators': 873}, 'status': 'ok'}


{'loss': 0.9642857142857143, 'params': {'class_weight': None, 'max_depth': 17, 'max_features': 'sqrt', 'max_leaf_nodes': 46, 'max_samples': 0.15866300388832533, 'min_impurity_decrease': 0.9297347852530089, 'min_samples_leaf': 7, 'min_samples_split': 6, 'min_weight_fraction_leaf': 0.18404233693328886, 'n_estimators': 121}, 'status': 'ok'}


{'loss': nan, 'params': {'class_weight': 'balanced', 'max_depth': 7, 'max_features': 'None', 'max_leaf_nodes': 104, 'max_samples': 0.0367072640631847, 'min_impurity_decrease': 0.12910648344978914, 'min_samples_leaf': 2, 'min_samples_split': 15, 'min_weight_fraction_leaf': 0.3161712810846662, 'n_estimators': 767}, 'status': 'ok'}


{'loss': nan, 'params': {'class_weight': 'balanced', 'max_depth': 3, 'max_features': 'None', 'max_leaf_nodes': 124, 'max_samples': 0.16440865223966705, 'min_impurity_decrease': 0.391904635576072, 'min_samples_leaf': 1, 'min_samples_split': 7, 'min_weight_fraction_leaf': 0.0811356314154057, 'n_estimators': 347}, 'status': 'ok'}


{'loss': nan, 'params': {'class_weight': 'balanced', 'max_depth': 12, 'max_features': 'log2', 'max_leaf_nodes': 68, 'max_samples': 0.8502406812728349, 'min_impurity_decrease': 0.7058978690401395, 'min_samples_leaf': 2, 'min_samples_split': 16, 'min_weight_fraction_leaf': 0.7016784424128134, 'n_estimators': 938}, 'status': 'ok'}


{'loss': nan, 'params': {'class_weight': 'balanced', 'max_depth': 5, 'max_features': 'log2', 'max_leaf_nodes': 99, 'max_samples': 0.23705851369580344, 'min_impurity_decrease': 0.20836965887913506, 'min_samples_leaf': 7, 'min_samples_split': 3, 'min_weight_fraction_leaf': 0.7453528956610014, 'n_estimators': 468}, 'status': 'ok'}


{'loss': nan, 'params': {'class_weight': None, 'max_depth': 15, 'max_features': 'None', 'max_leaf_nodes': 114, 'max_samples': 0.7084444118326696, 'min_impurity_decrease': 0.986092424730284, 'min_samples_leaf': 3, 'min_samples_split': 14, 'min_weight_fraction_leaf': 0.30715124274867167, 'n_estimators': 743}, 'status': 'ok'}


{'loss': 0.9642857142857143, 'params': {'class_weight': 'balanced', 'max_depth': 10, 'max_features': 'sqrt', 'max_leaf_nodes': 97, 'max_samples': 0.9199683481619908, 'min_impurity_decrease': 0.34148971488668467, 'min_samples_leaf': 5, 'min_samples_split': 10, 'min_weight_fraction_leaf': 0.006984816385200432, 'n_estimators': 386}, 'status': 'ok'}


{'loss': nan, 'params': {'class_weight': None, 'max_depth': 13, 'max_features': 'None', 'max_leaf_nodes': 20, 'max_samples': 0.38036460187991084, 'min_impurity_decrease': 0.8852038598514178, 'min_samples_leaf': 5, 'min_samples_split': 11, 'min_weight_fraction_leaf': 0.06166031048348186, 'n_estimators': 635}, 'status': 'ok'}


{'loss': nan, 'params': {'class_weight': 'balanced', 'max_depth': 5, 'max_features': 'None', 'max_leaf_nodes': 52, 'max_samples': 0.8640312159272309, 'min_impurity_decrease': 0.16823848137945396, 'min_samples_leaf': 1, 'min_samples_split': 9, 'min_weight_fraction_leaf': 0.24162088495434908, 'n_estimators': 564}, 'status': 'ok'}


{'status': 'new'}


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I executed the 'fmin' for full iteration but same result. What am I doing Wrong here ???

score 0 · Answer 1 · answered Jul 04 '20 at 14:29

0

Your loss in Objective_Forest is defined as (1 - np.mean(score), and score is evaluated by cross validation with cross_val_score. The loss, therefore, depends on the output of your cross_val_score function. You are using scorer as the evaluation metric in cross_val_score, but you have not defined it anywhere in your code (was probably defined somewhere else). Your NaN values are most likely due to the kind of scoring you are implementing in your cross validation.

def Objective_Forest(params):
    classifier = RandomForestClassifier(**params)
    score = cross_val_score(classifier,train,Target,cv = 10,scoring = scorer)
    Best_Score = 1 - np.mean(score)
    return {'loss': Best_Score,'params':params,'status':STATUS_OK}

answered Jul 04 '20 at 14:29

ProteinGuy

1,754
2
17
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Thanks for your reply. I have defined a scoring function as: scorer = make_scorer(f1_score,greater_is_better = 'True',average = 'weighted') . I dont know where I am doing wrong. – Jul 06 '20 at 05:30
The F1 score is the harmonic mean of precision and recall. If your recall is zero, you will be dividing by zero to calculate the F1, and so your result will be NaN. I suggest you use another scoring metric that is not unstable at zero values (like `sklearn.matthews_corrcoef`). – ProteinGuy Jul 06 '20 at 10:08

Why hyperopt is giving the best loss Nan while operating in Random Forest?

1 Answers1