yhat = model3.transform(tmpX[n_train:], tmpY[n_train:])
model3.zscore(yhat, tmpY[n_train:])
print(f"{c*10} End Model3 <-- new, hyperparams {c*10}\n")
# ## TODO: classifier/regressor/clusterer/etc Mixin requirements
# piper = Pipeline(['model', model2])
# print( piper )
# piper.fit_transform(tmpX, tmpY)
print(f"\n{c*10} Starting TrainingManager with Grid Search {c*10}\n")
import preprocess, extract
from sklearn.preprocessing import StandardScaler, PowerTransformer
from sklearn.linear_model import LogisticRegression
from sklearn import svm
dpipez = [Pipeline([('scaler', StandardScaler()), ]),
Pipeline([('power', PowerTransformer()),])
]
mpipez = [ ( Pipeline([ ('flatten', preprocess.Flattenor()), ('svm', svm.SVC() ) ]), {'kernel':('linear', 'rbf'), 'C':[1, 10]}) , ##
( Pipeline([ ('flatten', preprocess.Flattenor()),('logit', LogisticRegression() ) ]), {'C':[1,10]} ), ##
(Pipeline([('reshaper', preprocess.Reshapeor( (1, -1)) ), ('tensorfy', preprocess.ToTensor() ),('zmodel', model2)]), {})
] #*tmpX[0].shape
print( mpipez)
mgr = ZTrainingManager()
mgr.build_permutationz(data_pipez=dpipez, model_pipez=mpipez)
mgr.run( [x.cpu().numpy().ravel() for x in tmpX], [y.cpu().numpy().ravel() for y in tmpY] , train_test_split=1.)
print(f"{c*10} End ZTrainingManager {c*10}\n")
print(f"\n{c*10} Starting TrainingManager with Grid Search {c*10}\n")
import preprocess, extract
from sklearn.preprocessing import StandardScaler, PowerTransformer
from sklearn.linear_model import LogisticRegression
from sklearn import svm
dpipez = [
Pipeline([
('scaler', StandardScaler()),
]),
Pipeline([
('power', PowerTransformer()),
])
]
mpipez = [
(Pipeline([('flatten', preprocess.Flattenor()), ('svm', svm.SVC())]), {
'kernel': ('linear', 'rbf'),
'C': [1, 10]
}), ##
(Pipeline([('flatten', preprocess.Flattenor()),
('logit', LogisticRegression())]), {
'C': [1, 10]
}), ##
(Pipeline([('reshaper', preprocess.Reshapeor((1, -1))),
('tensorfy', preprocess.ToTensor()),
('zmodel', model2)]), {})
] #*tmpX[0].shape
print(mpipez)
mgr = ZTrainingManager()
ftype=zdata.PdDataStats.TYPE_TXT_LINES_FILE)
dframe = pdstats.dframe.sample(n=130)
X_data = dframe
y_data = dframe['Normal'].values.astype(np.float32) ##TODO: 'dcodez_short'
print("Loaded into PdFrame data of size: ", len(dframe),
" and into y_data of size ", len(y_data))
print(dframe.columns)
### Setup y_label : n-ary classification
## 2. PIPELINEZ
loader_p = [
('fetch_img',
preprocess.LoadImageFileTransform('fpath', crop_ratio=0.75)),
]
reshapeor_1 = [
('flatten', preprocess.Flattenor()),
]
funduzor_1 = [
('funduzor', extract.FundusColorChannelz()),
]
scaler_p = [
('scaler', StandardScaler()),
]
tmpz = Pipeline(loader_p + funduzor_1).transform(X_data)
print(len(tmpz), tmpz[0].shape)
# _ = [print(f"{t.shape}") for t in tmpz]
utilz.Image.plot_images_list([t[:, :, 1:] for t in tmpz], nc=5, cmap=None)