Commit 62b77bc2 authored by Simon Clarke's avatar Simon Clarke
Browse files

Final version of Imputation notebook.

parent 1adb9ff8
......@@ -10,10 +10,26 @@
Previously, we have dealt with missing data by deleting that entry. However, that means losing valuable data which contributes to the training of your model. A better approach is to impute the data, i.e., infer the missing data from the existing observations.
We will concentrate here on Scikit-Learn's imputation routines, although some of the techniques, such as replacement of values with the mean or mode, can be easily implemented in Pandas.
%% Cell type:markdown id: tags:
## Contents
%% Cell type:markdown id: tags:
* Introduction
* Cross-validation analysis
* Exercises
%% Cell type:markdown id: tags:
## Introduction
%% Cell type:markdown id: tags:
We first import the standard libraries and the csv file.
%% Cell type:code id: tags:
``` python
......@@ -38,56 +54,56 @@
```
%%%% Output: execute_result
Pregnancies Glucose BloodPressure SkinThickness Insulin BMI \
480 3 158 70 30 328 35.5
652 5 123 74 40 77 34.1
219 5 112 66 0 0 37.8
205 5 111 72 28 0 23.9
359 1 196 76 36 249 36.5
672 10 68 106 23 49 35.5
402 5 136 84 41 88 35.0
626 0 125 68 0 0 24.7
564 0 91 80 0 0 32.4
34 10 122 78 31 0 27.6
68 1 95 66 13 38 19.6
639 1 100 74 12 46 19.5
400 4 95 64 0 0 32.0
314 7 109 80 31 0 35.9
508 2 84 50 23 76 30.4
105 1 126 56 29 152 28.7
35 4 103 60 33 192 24.0
164 0 131 88 0 0 31.6
244 2 146 76 35 194 38.2
220 0 177 60 29 478 34.6
11 10 168 74 0 0 38.0
111 8 155 62 26 495 34.0
186 8 181 68 36 495 30.1
396 3 96 56 34 115 24.7
258 1 193 50 16 375 25.9
26 7 147 76 0 0 39.4
679 2 101 58 17 265 24.2
139 5 105 72 29 325 36.9
269 2 146 0 0 0 27.5
589 0 73 0 0 0 21.1
547 4 131 68 21 166 33.1
401 6 137 61 0 0 24.2
168 4 110 66 0 0 31.9
12 10 139 80 0 0 27.1
412 1 143 84 23 310 42.4
6 3 78 50 32 88 31.0
153 1 153 82 42 485 40.6
71 5 139 64 35 140 28.6
632 2 111 60 0 0 26.2
DiabetesPedigreeFunction Age Outcome
480 0.344 35 1
652 0.269 28 0
219 0.261 41 1
205 0.407 27 0
359 0.875 29 1
672 0.285 47 0
402 0.286 35 1
626 0.206 21 0
564 0.601 27 0
34 0.512 45 0
68 0.334 25 0
639 0.149 28 0
400 0.161 31 1
314 1.127 43 1
508 0.968 21 0
105 0.801 21 0
35 0.966 33 0
164 0.743 32 1
244 0.329 29 0
220 1.072 21 1
11 0.537 34 1
111 0.543 46 1
186 0.615 60 1
396 0.944 39 0
258 0.655 24 0
26 0.257 43 1
679 0.614 23 0
139 0.159 28 0
269 0.240 28 1
589 0.342 25 0
547 0.160 28 0
401 0.151 55 0
168 0.471 29 0
12 1.441 57 0
412 1.076 22 0
6 0.248 26 1
153 0.687 23 0
71 0.411 26 0
632 0.343 23 0
%% Cell type:markdown id: tags:
This can be investigated further by displaying the descriptive statistics, for which it is apparent that `Glucose` and `BMI` also have unrealistic values of 0. A value of `Pregnancies` of 0, is a physically realistic value.
This can be investigated further by displaying the descriptive statistics, for which it is apparent that `Glucose` and `BMI` also have unrealistic values of 0. A value of 0 for `Pregnancies` is a physically realistic value.
%% Cell type:code id: tags:
``` python
pima.describe()
......@@ -150,13 +166,13 @@
def rf_model(pimadf):
Xf = pimadf.drop(columns=['Outcome'])
Yf = np.ravel(pimadf[['Outcome']])
X_train, X_test, Y_train, Y_test = train_test_split(Xf,Yf,test_size=0.8,random_state=0)
X_train, X_test, Y_train, Y_test = train_test_split(Xf, Yf, test_size=0.2, random_state=0)
rfc = RandomForestClassifier()
rfc.fit(X_train,Y_train) # fit the data to the model
rfc.fit(X_train, Y_train) # fit the data to the model
Y_pred = rfc.predict(X_test)
acc = accuracy_score(Y_test,Y_pred)
print("Testing score is %5.3f" % acc)
feature_importances = pd.DataFrame(rfc.feature_importances_,
......@@ -280,11 +296,11 @@
The second method we consider is the sklearn `IterativeImputer`. This is an experimental addition to sklearn, so needs to be enabled as well as imported. As it is experimental, it may change in future versions.
`IterativeImputer` works be marking the missing values, and then repeating the imputation process N times or until the data converges. Initially the missing values are set using a simple scheme, such as being replaced by the mean or median. Then on each iteration a machine learning algorithm is used as a regressor to update each column which is marked as having missing values. The non-missing values are used to train the model, and then the model is used to predict the missing values. Any regression technique could be used to predict the missing values. Common ones that are used are BayesianRidge, k-Nearest Neighbours and Random Forest Regression. Using this algorithm with Random Forest Regression is equivalent to the R routine `missForest`. The routine `KNNImputer` can be seen as `IterativeImputer` with one iteration.
In this example, we use the default algorithm, BayesianRidge. This gives that the testing score slightly, however the feature importance is consistent with the original dataset and the results of ``KNNImputer`.
In this example, we use the default algorithm, BayesianRidge. This gives that the testing score slightly, however the feature importance is consistent with the original dataset and the results of `KNNImputer`.
%% Cell type:code id: tags:
``` python
from sklearn.experimental import enable_iterative_imputer
......@@ -341,10 +357,14 @@
![](data:image/png;base64,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)
%% Cell type:markdown id: tags:
## Cross-validation analysis
%% Cell type:markdown id: tags:
For all the examples so far we have only consider one realisation of the Random Forest Regressor. To understand the effectiveness of the various imputation algorithms we need to combine this with cross validation. The following code consider the variation of the f1-score using Logistic Regression for the imputation strategies:
* Drop rows with missing values.
* Simple imputation using the mean.
* Simple imputation using the median.
* k-Nearest Neighbours imputation.
......@@ -359,25 +379,25 @@
%% Cell type:code id: tags:
``` python
from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import RepeatedKFold
N_SPLITS = 10
N_SPLITS = RepeatedKFold(n_splits=5, n_repeats=3, random_state=1)
classifier = LogisticRegression(solver='newton-cg', C=1.e3)
score = 'f1'
X_full = pima_drop.drop(columns=['Outcome'])
Y_full = np.ravel(pima_drop[['Outcome']])
lr_estimator = LogisticRegression(solver='newton-cg', C=1.e3)
score_drop = pd.DataFrame(
cross_val_score(
lr_estimator, X_full, Y_full, scoring='f1', cv=N_SPLITS
classifier, X_full, Y_full, scoring=score, cv=N_SPLITS
),
columns=['Drop Data']
)
```
%% Cell type:code id: tags:
``` python
......@@ -390,15 +410,14 @@
score_simple_imputer = pd.DataFrame()
for strategy in ('mean', 'median'):
estimator = make_pipeline(
SimpleImputer(missing_values=np.nan, strategy=strategy),
lr_estimator
classifier
)
score_simple_imputer[strategy] = cross_val_score(
estimator, X_missing, Y_missing, scoring='f1',
cv=N_SPLITS
estimator, X_missing, Y_missing, scoring=score, cv=N_SPLITS
)
```
%% Cell type:code id: tags:
......@@ -407,15 +426,14 @@
from sklearn.impute import KNNImputer
score_knn_imputer = pd.DataFrame()
estimator = make_pipeline(
KNNImputer(n_neighbors=15),
rf_estimator
classifier
)
score_knn_imputer['KNeighborsRegressor'] = cross_val_score(
estimator, X_missing, Y_missing, scoring='f1',
cv=N_SPLITS
estimator, X_missing, Y_missing, scoring=score, cv=N_SPLITS
)
```
%% Cell type:code id: tags:
......@@ -434,15 +452,15 @@
]
score_iterative_imputer = pd.DataFrame()
for impute_estimator in estimators:
estimator = make_pipeline(
IterativeImputer(random_state=0, estimator=impute_estimator, max_iter=10),
lr_estimator
classifier
)
score_iterative_imputer[impute_estimator.__class__.__name__] = \
cross_val_score(
estimator, X_missing, Y_missing, scoring='f1', cv=N_SPLITS
estimator, X_missing, Y_missing, scoring=score, cv=N_SPLITS
)
```
%% Cell type:markdown id: tags:
......@@ -472,40 +490,40 @@
%%%% Output: execute_result
Original SimpleImputer KNN \
Drop Data mean median KNeighborsRegressor
count 10.000000 10.000000 10.000000 10.000000
mean 0.621762 0.626776 0.632280 0.626371
std 0.132392 0.062190 0.055559 0.059190
min 0.352941 0.520000 0.541667 0.520000
25% 0.543478 0.605662 0.605662 0.605662
50% 0.641026 0.632874 0.640256 0.626225
75% 0.718531 0.663265 0.666667 0.663265
max 0.782609 0.727273 0.727273 0.711111
count 15.000000 15.000000 15.000000 15.000000
mean 0.625292 0.632006 0.630530 0.634507
std 0.093487 0.028496 0.028269 0.029221
min 0.387097 0.589474 0.589474 0.589474
25% 0.588040 0.606338 0.604167 0.605114
50% 0.641509 0.639175 0.633663 0.640777
75% 0.673333 0.653870 0.648134 0.659567
max 0.740741 0.675000 0.675000 0.674157
IterativeImputer \
BayesianRidge DecisionTreeRegressor RandomForestRegressor
count 10.000000 10.000000 10.000000
mean 0.635335 0.629131 0.633366
std 0.056181 0.059494 0.049522
min 0.541667 0.541667 0.541667
25% 0.613384 0.588889 0.612077
50% 0.637331 0.625000 0.645680
75% 0.663265 0.663462 0.663462
max 0.727273 0.739130 0.711111
count 15.000000 15.000000 15.000000
mean 0.637322 0.626273 0.633008
std 0.032623 0.033283 0.028420
min 0.589474 0.571429 0.589474
25% 0.608206 0.600885 0.610594
50% 0.633663 0.631579 0.628571
75% 0.663522 0.654536 0.659471
max 0.688889 0.673913 0.674157
KNeighborsRegressor
count 10.000000
mean 0.627487
std 0.057144
min 0.541667
25% 0.584496
50% 0.626225
75% 0.666667
max 0.711111
count 15.000000
mean 0.627713
std 0.024185
min 0.589474
25% 0.605454
50% 0.629213
75% 0.649111
max 0.659091
%% Cell type:code id: tags:
``` python
# plot results
......@@ -516,16 +534,111 @@
plt.show()
```
%%%% Output: display_data
![](data:image/png;base64,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)
![](data:image/png;base64,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)
%% Cell type:markdown id: tags:
## Exercises
%% Cell type:markdown id: tags:
For the exercises we will use the [Abalone Dataset](https://archive.ics.uci.edu/ml/datasets/Abalone), which can be downloaded from [Monash Gitlab](https://gitlab.erc.monash.edu.au/bads/data-challenges-resources/-/tree/main/Machine-Learning/Imputation/abalone.csv). This consists of physical measurements of abalones from the Tasmanian coast in the 1990s, in an effort to determine their age. Previously the age would need to be determined in the laboratory by counting the number of rings in the shell. Then $Age = Rings + 1.5$. This is a complete dataset, however we will randomly remove entries in two columns to perform imputation.
First we load the dataset.
%% Cell type:code id: tags:
``` python
abalone = pd.read_csv("abalone.csv")
abalone.head()
```
%% Cell type:markdown id: tags:
The `Sex` field has three categorical entries: Male (M), Female (F) and Infant (I). Se we need to one-hot encode these fields to create three binary columns.
%% Cell type:code id: tags:
``` python
dummy = pd.get_dummies(abalone['Sex'])
abalone = pd.concat([abalone, dummy], axis=1)
abalone.drop(columns=['Sex'], inplace=True)
abalone.head()
```
%%%% Output: execute_result
Length Diameter Height Whole weight Shucked weight Viscera weight \
0 0.455 0.365 0.095 0.5140 0.2245 0.1010
1 0.350 0.265 0.090 0.2255 0.0995 0.0485
2 0.530 0.420 0.135 0.6770 0.2565 0.1415
3 0.440 0.365 0.125 0.5160 0.2155 0.1140
4 0.330 0.255 0.080 0.2050 0.0895 0.0395
Shell weight Rings F I M
0 0.150 15 0 0 1
1 0.070 7 0 0 1
2 0.210 9 1 0 0
3 0.155 10 0 0 1
4 0.055 7 0 1 0
%% Cell type:markdown id: tags:
Last we create a features array (Xf) and a label array (Yf). Then we randomly remove 33% of the `Height` samples and 25% of the `Shell weight` samples from the features array.
%% Cell type:code id: tags:
``` python
Xf = abalone.drop(columns=['Rings'])
Yf = abalone[['Rings']]
X = Xf.copy()
X['Height'] = X['Height'].sample(frac=0.67)
X['Shell weight'] = X['Shell weight'].sample(frac=0.75)
X.describe()
```
%% Cell type:markdown id: tags:
### Exercise 1 (2 marks)
%% Cell type:markdown id: tags:
Create a Random Forest Regressor model for the full dataset and determine the accuracy of this model. Use an 80:20 split for training and testing.
%% Cell type:code id: tags:
``` python
```
%% Cell type:markdown id: tags:
### Exercise 2 (3 marks)
%% Cell type:markdown id: tags:
Fill in the missing values of X using `IterativeImputer` with 10 iterations and using the `BayesianRidge` regressor. Calculate the accuracy of the Random Forest Regressor using this imputed dataset.
%% Cell type:code id: tags:
``` python
```
%% Cell type:markdown id: tags:
### Exercise 3 (5 marks)
%% Cell type:markdown id: tags:
Fill in the missing values of X using `IterativeImputer` with 10 iterations and using `KNeighborsRegressor` for 5, 10, 15 and 20 neighbours. Calculate the accuracy of the Random Forest Regressor using each of these imputed datasets.
%% Cell type:code id: tags:
``` python
```
......
Sex,Length,Diameter,Height,Whole weight,Shucked weight,Viscera weight,Shell weight,Rings
M,0.455,0.365,0.095,0.514,0.2245,0.101,0.15,15
M,0.35,0.265,0.09,0.2255,0.0995,0.0485,0.07,7
F,0.53,0.42,0.135,0.677,0.2565,0.1415,0.21,9
M,0.44,0.365,0.125,0.516,0.2155,0.114,0.155,10
I,0.33,0.255,0.08,0.205,0.0895,0.0395,0.055,7
I,0.425,0.3,0.095,0.3515,0.141,0.0775,0.12,8
F,0.53,0.415,0.15,0.7775,0.237,0.1415,0.33,20
F,0.545,0.425,0.125,0.768,0.294,0.1495,0.26,16
M,0.475,0.37,0.125,0.5095,0.2165,0.1125,0.165,9
F,0.55,0.44,0.15,0.8945,0.3145,0.151,0.32,19
F,0.525,0.38,0.14,0.6065,0.194,0.1475,0.21,14
M,0.43,0.35,0.11,0.406,0.1675,0.081,0.135,10
M,0.49,0.38,0.135,0.5415,0.2175,0.095,0.19,11
F,0.535,0.405,0.145,0.6845,0.2725,0.171,0.205,10
F,0.47,0.355,0.1,0.4755,0.1675,0.0805,0.185,10
M,0.5,0.4,0.13,0.6645,0.258,0.133,0.24,12
I,0.355,0.28,0.085,0.2905,0.095,0.0395,0.115,7
F,0.44,0.34,0.1,0.451,0.188,0.087,0.13,10
M,0.365,0.295,0.08,0.2555,0.097,0.043,0.1,7
M,0.45,0.32,0.1,0.381,0.1705,0.075,0.115,9
M,0.355,0.28,0.095,0.2455,0.0955,0.062,0.075,11
I,0.38,0.275,0.1,0.2255,0.08,0.049,0.085,10
F,0.565,0.44,0.155,0.9395,0.4275,0.214,0.27,12
F,0.55,0.415,0.135,0.7635,0.318,0.21,0.2,9
F,0.615,0.48,0.165,1.1615,0.513,0.301,0.305,10
F,0.56,0.44,0.14,0.9285,0.3825,0.188,0.3,11
F,0.58,0.45,0.185,0.9955,0.3945,0.272,0.285,11
M,0.59,0.445,0.14,0.931,0.356,0.234,0.28,12
M,0.605,0.475,0.18,0.9365,0.394,0.219,0.295,15
M,0.575,0.425,0.14,0.8635,0.393,0.227,0.2,11
M,0.58,0.47,0.165,0.9975,0.3935,0.242,0.33,10
F,0.68,0.56,0.165,1.639,0.6055,0.2805,0.46,15
M,0.665,0.525,0.165,1.338,0.5515,0.3575,0.35,18
F,0.68,0.55,0.175,1.798,0.815,0.3925,0.455,19
F,0.705,0.55,0.2,1.7095,0.633,0.4115,0.49,13
M,0.465,0.355,0.105,0.4795,0.227,0.124,0.125,8
F,0.54,0.475,0.155,1.217,0.5305,0.3075,0.34,16
F,0.45,0.355,0.105,0.5225,0.237,0.1165,0.145,8
F,0.575,0.445,0.135,0.883,0.381,0.2035,0.26,11
M,0.355,0.29,0.09,0.3275,0.134,0.086,0.09,9
F,0.45,0.335,0.105,0.425,0.1865,0.091,0.115,9
F,0.55,0.425,0.135,0.8515,0.362,0.196,0.27,14
I,0.24,0.175,0.045,0.07,0.0315,0.0235,0.02,5
I,0.205,0.15,0.055,0.042,0.0255,0.015,0.012,5
I,0.21,0.15,0.05,0.042,0.0175,0.0125,0.015,4
I,0.39,0.295,0.095,0.203,0.0875,0.045,0.075,7
M,0.47,0.37,0.12,0.5795,0.293,0.227,0.14,9
F,0.46,0.375,0.12,0.4605,0.1775,0.11,0.15,7
I,0.325,0.245,0.07,0.161,0.0755,0.0255,0.045,6
F,0.525,0.425,0.16,0.8355,0.3545,0.2135,0.245,9
I,0.52,0.41,0.12,0.595,0.2385,0.111,0.19,8
M,0.4,0.32,0.095,0.303,0.1335,0.06,0.1,7
M,0.485,0.36,0.13,0.5415,0.2595,0.096,0.16,10
F,0.47,0.36,0.12,0.4775,0.2105,0.1055,0.15,10
M,0.405,0.31,0.1,0.385,0.173,0.0915,0.11,7
F,0.5,0.4,0.14,0.6615,0.2565,0.1755,0.22,8
M,0.445,0.35,0.12,0.4425,0.192,0.0955,0.135,8
M,0.47,0.385,0.135,0.5895,0.2765,0.12,0.17,8
I,0.245,0.19,0.06,0.086,0.042,0.014,0.025,4
F,0.505,0.4,0.125,0.583,0.246,0.13,0.175,7
M,0.45,0.345,0.105,0.4115,0.18,0.1125,0.135,7
M,0.505,0.405,0.11,0.625,0.305,0.16,0.175,9
F,0.53,0.41,0.13,0.6965,0.302,0.1935,0.2,10
M,0.425,0.325,0.095,0.3785,0.1705,0.08,0.1,7
M,0.52,0.4,0.12,0.58,0.234,0.1315,0.185,8
M,0.475,0.355,0.12,0.48,0.234,0.1015,0.135,8
F,0.565,0.44,0.16,0.915,0.354,0.1935,0.32,12
F,0.595,0.495,0.185,1.285,0.416,0.224,0.485,13
F,0.475,0.39,0.12,0.5305,0.2135,0.1155,0.17,10
I,0.31,0.235,0.07,0.151,0.063,0.0405,0.045,6
M,0.555,0.425,0.13,0.7665,0.264,0.168,0.275,13
F,0.4,0.32,0.11,0.353,0.1405,0.0985,0.1,8
F,0.595,0.475,0.17,1.247,0.48,0.225,0.425,20
M,0.57,0.48,0.175,1.185,0.474,0.261,0.38,11
F,0.605,0.45,0.195,1.098,0.481,0.2895,0.315,13
F,0.6,0.475,0.15,1.0075,0.4425,0.221,0.28,15
M,0.595,0.475,0.14,0.944,0.3625,0.189,0.315,9
F,0.6,0.47,0.15,0.922,0.363,0.194,0.305,10
F,0.555,0.425,0.14,0.788,0.282,0.1595,0.285,11
F,0.615,0.475,0.17,1.1025,0.4695,0.2355,0.345,14
F,0.575,0.445,0.14,0.941,0.3845,0.252,0.285,9
M,0.62,0.51,0.175,1.615,0.5105,0.192,0.675,12
F,0.52,0.425,0.165,0.9885,0.396,0.225,0.32,16
M,0.595,0.475,0.16,1.3175,0.408,0.234,0.58,21
M,0.58,0.45,0.14,1.013,0.38,0.216,0.36,14
F,0.57,0.465,0.18,1.295,0.339,0.2225,0.44,12
M,0.625,0.465,0.14,1.195,0.4825,0.205,0.4,13
M,0.56,0.44,0.16,0.8645,0.3305,0.2075,0.26,10
F,0.46,0.355,0.13,0.517,0.2205,0.114,0.165,9
F,0.575,0.45,0.16,0.9775,0.3135,0.231,0.33,12
M,0.565,0.425,0.135,0.8115,0.341,0.1675,0.255,15
M,0.555,0.44,0.15,0.755,0.307,0.1525,0.26,12
M,0.595,0.465,0.175,1.115,0.4015,0.254,0.39,13
F,0.625,0.495,0.165,1.262,0.507,0.318,0.39,10
M,0.695,0.56,0.19,1.494,0.588,0.3425,0.485,15
M,0.665,0.535,0.195,1.606,0.5755,0.388,0.48,14
M,0.535,0.435,0.15,0.725,0.269,0.1385,0.25,9
M,0.47,0.375,0.13,0.523,0.214,0.132,0.145,8
M,0.47,0.37,0.13,0.5225,0.201,0.133,0.165,7
F,0.475,0.375,0.125,0.5785,0.2775,0.085,0.155,10
I,0.36,0.265,0.095,0.2315,0.105,0.046,0.075,7
M,0.55,0.435,0.145,0.843,0.328,0.1915,0.255,15
M,0.53,0.435,0.16,0.883,0.316,0.164,0.335,15
M,0.53,0.415,0.14,0.724,0.3105,0.1675,0.205,10
M,0.605,0.47,0.16,1.1735,0.4975,0.2405,0.345,12
F,0.52,0.41,0.155,0.727,0.291,0.1835,0.235,12
F,0.545,0.43,0.165,0.802,0.2935,0.183,0.28,11
F,0.5,0.4,0.125,0.6675,0.261,0.1315,0.22,10
F,0.51,0.39,0.135,0.6335,0.231,0.179,0.2,9
F,0.435,0.395,0.105,0.3635,0.136,0.098,0.13,9
M,0.495,0.395,0.125,0.5415,0.2375,0.1345,0.155,9
M,0.465,0.36,0.105,0.431,0.172,0.107,0.175,9
I,0.435,0.32,0.08,0.3325,0.1485,0.0635,0.105,9
M,0.425,0.35,0.105,0.393,0.13,0.063,0.165,9
F,0.545,0.41,0.125,0.6935,0.2975,0.146,0.21,11
F,0.53,0.415,0.115,0.5915,0.233,0.1585,0.18,11
F,0.49,0.375,0.135,0.6125,0.2555,0.102,0.22,11
M,0.44,0.34,0.105,0.402,0.1305,0.0955,0.165,10
F,0.56,0.43,0.15,0.8825,0.3465,0.172,0.31,9
M,0.405,0.305,0.085,0.2605,0.1145,0.0595,0.085,8
F,0.47,0.365,0.105,0.4205,0.163,0.1035,0.14,9
I,0.385,0.295,0.085,0.2535,0.103,0.0575,0.085,7
F,0.515,0.425,0.14,0.766,0.304,0.1725,0.255,14
M,0.37,0.265,0.075,0.214,0.09,0.051,0.07,6
I,0.36,0.28,0.08,0.1755,0.081,0.0505,0.07,6
I,0.27,0.195,0.06,0.073,0.0285,0.0235,0.03,5
I,0.375,0.275,0.09,0.238,0.1075,0.0545,0.07,6
I,0.385,0.29,0.085,0.2505,0.112,0.061,0.08,8