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udemy-ML/16-Boosted-Trees/.ipynb_checkpoints/00-AdaBoost-checkpoint.ipynb

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Copyright by Pierian Data Inc. For more information, visit us at www.pieriandata.com

AdaBoost

The Data

Mushroom Hunting: Edible or Poisonous?

Data Source: https://archive.ics.uci.edu/ml/datasets/Mushroom

This data set includes descriptions of hypothetical samples corresponding to 23 species of gilled mushrooms in the Agaricus and Lepiota Family (pp. 500-525). Each species is identified as definitely edible, definitely poisonous, or of unknown edibility and not recommended. This latter class was combined with the poisonous one. The Guide clearly states that there is no simple rule for determining the edibility of a mushroom; no rule like ``leaflets three, let it be'' for Poisonous Oak and Ivy.

Attribute Information:

  1. cap-shape: bell=b,conical=c,convex=x,flat=f, knobbed=k,sunken=s
  2. cap-surface: fibrous=f,grooves=g,scaly=y,smooth=s
  3. cap-color: brown=n,buff=b,cinnamon=c,gray=g,green=r, pink=p,purple=u,red=e,white=w,yellow=y
  4. bruises?: bruises=t,no=f
  5. odor: almond=a,anise=l,creosote=c,fishy=y,foul=f, musty=m,none=n,pungent=p,spicy=s
  6. gill-attachment: attached=a,descending=d,free=f,notched=n
  7. gill-spacing: close=c,crowded=w,distant=d
  8. gill-size: broad=b,narrow=n
  9. gill-color: black=k,brown=n,buff=b,chocolate=h,gray=g, green=r,orange=o,pink=p,purple=u,red=e, white=w,yellow=y
  10. stalk-shape: enlarging=e,tapering=t
  11. stalk-root: bulbous=b,club=c,cup=u,equal=e, rhizomorphs=z,rooted=r,missing=?
  12. stalk-surface-above-ring: fibrous=f,scaly=y,silky=k,smooth=s
  13. stalk-surface-below-ring: fibrous=f,scaly=y,silky=k,smooth=s
  14. stalk-color-above-ring: brown=n,buff=b,cinnamon=c,gray=g,orange=o, pink=p,red=e,white=w,yellow=y
  15. stalk-color-below-ring: brown=n,buff=b,cinnamon=c,gray=g,orange=o, pink=p,red=e,white=w,yellow=y
  16. veil-type: partial=p,universal=u
  17. veil-color: brown=n,orange=o,white=w,yellow=y
  18. ring-number: none=n,one=o,two=t
  19. ring-type: cobwebby=c,evanescent=e,flaring=f,large=l, none=n,pendant=p,sheathing=s,zone=z
  20. spore-print-color: black=k,brown=n,buff=b,chocolate=h,green=r, orange=o,purple=u,white=w,yellow=y
  21. population: abundant=a,clustered=c,numerous=n, scattered=s,several=v,solitary=y
  22. habitat: grasses=g,leaves=l,meadows=m,paths=p, urban=u,waste=w,woods=d

Goal

THIS IS IMPORTANT, THIS IS NOT OUR TYPICAL PREDICTIVE MODEL!

Our general goal here is to see if we can harness the power of machine learning and boosting to help create not just a predictive model, but a general guideline for features people should look out for when picking mushrooms.

Imports

In [1]:
import numpy as np
import pandas as pd

import matplotlib.pyplot as plt
import seaborn as sns
In [2]:
df = pd.read_csv("../DATA/mushrooms.csv")
In [3]:
df.head()
Out[3]:
class cap-shape cap-surface cap-color bruises odor gill-attachment gill-spacing gill-size gill-color ... stalk-surface-below-ring stalk-color-above-ring stalk-color-below-ring veil-type veil-color ring-number ring-type spore-print-color population habitat
0 p x s n t p f c n k ... s w w p w o p k s u
1 e x s y t a f c b k ... s w w p w o p n n g
2 e b s w t l f c b n ... s w w p w o p n n m
3 p x y w t p f c n n ... s w w p w o p k s u
4 e x s g f n f w b k ... s w w p w o e n a g

5 rows × 23 columns

EDA

In [4]:
sns.countplot(data=df,x='class')
Out[4]:
<AxesSubplot:xlabel='class', ylabel='count'>
In [5]:
df.describe()
Out[5]:
class cap-shape cap-surface cap-color bruises odor gill-attachment gill-spacing gill-size gill-color ... stalk-surface-below-ring stalk-color-above-ring stalk-color-below-ring veil-type veil-color ring-number ring-type spore-print-color population habitat
count 8124 8124 8124 8124 8124 8124 8124 8124 8124 8124 ... 8124 8124 8124 8124 8124 8124 8124 8124 8124 8124
unique 2 6 4 10 2 9 2 2 2 12 ... 4 9 9 1 4 3 5 9 6 7
top e x y n f n f c b b ... s w w p w o p w v d
freq 4208 3656 3244 2284 4748 3528 7914 6812 5612 1728 ... 4936 4464 4384 8124 7924 7488 3968 2388 4040 3148

4 rows × 23 columns

In [6]:
df.describe().transpose()
Out[6]:
count unique top freq
class 8124 2 e 4208
cap-shape 8124 6 x 3656
cap-surface 8124 4 y 3244
cap-color 8124 10 n 2284
bruises 8124 2 f 4748
odor 8124 9 n 3528
gill-attachment 8124 2 f 7914
gill-spacing 8124 2 c 6812
gill-size 8124 2 b 5612
gill-color 8124 12 b 1728
stalk-shape 8124 2 t 4608
stalk-root 8124 5 b 3776
stalk-surface-above-ring 8124 4 s 5176
stalk-surface-below-ring 8124 4 s 4936
stalk-color-above-ring 8124 9 w 4464
stalk-color-below-ring 8124 9 w 4384
veil-type 8124 1 p 8124
veil-color 8124 4 w 7924
ring-number 8124 3 o 7488
ring-type 8124 5 p 3968
spore-print-color 8124 9 w 2388
population 8124 6 v 4040
habitat 8124 7 d 3148
In [7]:
plt.figure(figsize=(14,6),dpi=200)
sns.barplot(data=df.describe().transpose().reset_index().sort_values('unique'),x='index',y='unique')
plt.xticks(rotation=90);

Train Test Split

In [8]:
X = df.drop('class',axis=1)
In [9]:
X = pd.get_dummies(X,drop_first=True)
In [10]:
y = df['class']
In [11]:
from sklearn.model_selection import train_test_split
In [12]:
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state=101)

Modeling

In [13]:
from sklearn.ensemble import AdaBoostClassifier
In [14]:
model = AdaBoostClassifier(n_estimators=1)
In [15]:
model.fit(X_train,y_train)
Out[15]:
AdaBoostClassifier(n_estimators=1)

Evaluation

In [16]:
from sklearn.metrics import classification_report,plot_confusion_matrix,accuracy_score
In [17]:
predictions = model.predict(X_test)
In [18]:
predictions
Out[18]:
array(['p', 'e', 'p', ..., 'p', 'p', 'e'], dtype=object)
In [19]:
print(classification_report(y_test,predictions))

              precision    recall  f1-score   support

           e       0.96      0.81      0.88       655
           p       0.81      0.96      0.88       564

    accuracy                           0.88      1219
   macro avg       0.88      0.88      0.88      1219
weighted avg       0.89      0.88      0.88      1219
In [20]:
model.feature_importances_
Out[20]:
array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
In [21]:
model.feature_importances_.argmax()
Out[21]:
22
In [22]:
X.columns[22]
Out[22]:
'odor_n'
In [23]:
sns.countplot(data=df,x='odor',hue='class')
Out[23]:
<AxesSubplot:xlabel='odor', ylabel='count'>

Analyzing performance as more weak learners are added.

In [24]:
len(X.columns)
Out[24]:
95
In [25]:
error_rates = []

for n in range(1,96):
    
    model = AdaBoostClassifier(n_estimators=n)
    model.fit(X_train,y_train)
    preds = model.predict(X_test)
    err = 1 - accuracy_score(y_test,preds)
    
    error_rates.append(err)
In [26]:
plt.plot(range(1,96),error_rates)
Out[26]:
[<matplotlib.lines.Line2D at 0x289c33b1f70>]
In [27]:
model
Out[27]:
AdaBoostClassifier(n_estimators=95)
In [28]:
model.feature_importances_
Out[28]:
array([0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.01052632, 0.        ,
       0.        , 0.01052632, 0.        , 0.        , 0.        ,
       0.01052632, 0.        , 0.05263158, 0.03157895, 0.03157895,
       0.        , 0.        , 0.06315789, 0.02105263, 0.        ,
       0.        , 0.        , 0.09473684, 0.09473684, 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.01052632, 0.01052632, 0.        , 0.        , 0.        ,
       0.06315789, 0.        , 0.        , 0.        , 0.        ,
       0.03157895, 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.06315789, 0.        , 0.        ,
       0.01052632, 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.01052632, 0.        , 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.05263158, 0.        , 0.16842105, 0.        , 0.10526316,
       0.        , 0.        , 0.04210526, 0.        , 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.01052632])
In [26]:
feats = pd.DataFrame(index=X.columns,data=model.feature_importances_,columns=['Importance'])
In [27]:
feats
Out[27]:
Importance
cap-shape_c 0.000000
cap-shape_f 0.000000
cap-shape_k 0.000000
cap-shape_s 0.000000
cap-shape_x 0.000000
... ...
habitat_l 0.000000
habitat_m 0.000000
habitat_p 0.000000
habitat_u 0.000000
habitat_w 0.010526

95 rows × 1 columns

In [28]:
imp_feats = feats[feats['Importance']>0]
In [31]:
imp_feats
Out[31]:
Importance
cap-color_c 0.010526
cap-color_n 0.010526
cap-color_w 0.010526
bruises_t 0.052632
odor_c 0.031579
odor_f 0.031579
odor_n 0.063158
odor_p 0.021053
gill-spacing_w 0.094737
gill-size_n 0.094737
stalk-shape_t 0.010526
stalk-root_b 0.010526
stalk-surface-above-ring_k 0.063158
stalk-surface-below-ring_y 0.031579
stalk-color-below-ring_n 0.063158
stalk-color-below-ring_w 0.010526
ring-number_t 0.010526
spore-print-color_r 0.052632
spore-print-color_w 0.168421
population_c 0.105263
population_v 0.042105
habitat_w 0.010526
In [32]:
imp_feats = imp_feats.sort_values("Importance")
In [33]:
plt.figure(figsize=(14,6),dpi=200)
sns.barplot(data=imp_feats.sort_values('Importance'),x=imp_feats.sort_values('Importance').index,y='Importance')

plt.xticks(rotation=90);
In [34]:
sns.countplot(data=df,x='habitat',hue='class')
Out[34]:
<AxesSubplot:xlabel='habitat', ylabel='count'>

Interesting to see how the importance of the features shift as more are allowed to be added in! But remember these are all weak learner stumps, and feature importance is available for all the tree methods!

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