Develop Reference

ValueError: X has 2 features, but DecisionTreeClassifier is expecting 321 features as input

I was making a simple RandomForest Classifier Model
My hobbies classification dataset looks like this :
Name Category
0 Acroyoga general
1 Tennis sports
2 Aerial silk general
3 Sociology Academic
4 Amateur radio general
I split it into test and training
Encoded usin OneHotEncoder
Trained it using model = RandomForectClassifier() and model.fit(X_train)
It performed very well on the test dataset with an accuracy of 100%
Now, I wanted the model to predict a custom input I give.
So, I did this :
my_test = [ 'ML','Cricket']
test = pd.DataFrame(my_test, columns = ['testing'])
a = pd.get_dummies(test, columns = ['Duh'])
model.predict(a)
But it is giving this error :
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-24-8c4c6395f80e> in <module>
----> 1 model.predict(a)
E:\Anaconda\lib\site-packages\sklearn\ensemble\_forest.py in predict(self, X)
628 The predicted classes.
629 """
--> 630 proba = self.predict_proba(X)
631
632 if self.n_outputs_ == 1:
E:\Anaconda\lib\site-packages\sklearn\ensemble\_forest.py in predict_proba(self, X)
672 check_is_fitted(self)
673 # Check data
--> 674 X = self._validate_X_predict(X)
675
676 # Assign chunk of trees to jobs
E:\Anaconda\lib\site-packages\sklearn\ensemble\_forest.py in _validate_X_predict(self, X)
420 check_is_fitted(self)
421
--> 422 return self.estimators_[0]._validate_X_predict(X, check_input=True)
423
424 #property
E:\Anaconda\lib\site-packages\sklearn\tree\_classes.py in _validate_X_predict(self, X, check_input)
400 """Validate the training data on predict (probabilities)."""
401 if check_input:
--> 402 X = self._validate_data(X, dtype=DTYPE, accept_sparse="csr",
403 reset=False)
404 if issparse(X) and (X.indices.dtype != np.intc or
E:\Anaconda\lib\site-packages\sklearn\base.py in _validate_data(self, X, y, reset, validate_separately, **check_params)
435
436 if check_params.get('ensure_2d', True):
--> 437 self._check_n_features(X, reset=reset)
438
439 return out
E:\Anaconda\lib\site-packages\sklearn\base.py in _check_n_features(self, X, reset)
363
364 if n_features != self.n_features_in_:
--> 365 raise ValueError(
366 f"X has {n_features} features, but {self.__class__.__name__} "
367 f"is expecting {self.n_features_in_} features as input.")
**ValueError: X has 2 features, but DecisionTreeClassifier is expecting 321 features as input.**

RuntimeError: Can't call numpy() on Tensor that requires grad. Use tensor.detach().numpy() instead

I am trying to solve an nlp problem where i am using pytorch.here i am using sklearn's StratifiedKFold module for cross_validation, in which got a runtime error during looping through folds.
error message
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-43-f1ccf940359e> in <module>
----> 1 oof_train(ds, cv, y, epochs = EPOCHES)
<ipython-input-42-eaeaa91076bd> in oof_train(ds, cv, y, epochs)
14 loss_fn = torch.nn.MSELoss()
15
---> 16 for fold, (train_idx, valid_idx) in enumerate(cv.split(range(len(ds)), y)):
17
18 train_ds = D.Subset(ds, train_idx)
/opt/conda/lib/python3.7/site-packages/sklearn/model_selection/_split.py in split(self, X, y, groups)
729 to an integer.
730 """
--> 731 y = check_array(y, ensure_2d=False, dtype=None)
732 return super().split(X, y, groups)
733
/opt/conda/lib/python3.7/site-packages/sklearn/utils/validation.py in inner_f(*args, **kwargs)
61 extra_args = len(args) - len(all_args)
62 if extra_args <= 0:
---> 63 return f(*args, **kwargs)
64
65 # extra_args > 0
/opt/conda/lib/python3.7/site-packages/sklearn/utils/validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)
614 array = array.astype(dtype, casting="unsafe", copy=False)
615 else:
--> 616 array = np.asarray(array, order=order, dtype=dtype)
617 except ComplexWarning as complex_warning:
618 raise ValueError("Complex data not supported\n"
/opt/conda/lib/python3.7/site-packages/numpy/core/_asarray.py in asarray(a, dtype, order)
81
82 """
---> 83 return array(a, dtype, copy=False, order=order)
84
85
/opt/conda/lib/python3.7/site-packages/torch/tensor.py in __array__(self, dtype)
628 return handle_torch_function(Tensor.__array__, relevant_args, self, dtype=dtype)
629 if dtype is None:
--> 630 return self.numpy()
631 else:
632 return self.numpy().astype(dtype, copy=False)
RuntimeError: Can't call numpy() on Tensor that requires grad. Use tensor.detach().numpy() instead.
piece of code where it showing errors
def oof_train(ds, cv, y, epochs = EPOCHES):
loss_fn = torch.nn.MSELoss()
for fold, (train_idx, valid_idx) in enumerate(cv.split(range(len(ds)), y)):
train_ds = D.Subset(ds, train_idx)
loader = D.DataLoader(train_ds, batch_size=BATCH_SIZE,
shuffle=True, collate_fn = collate_fn,num_workers=0)
valid_ds = D.Subset(ds, valid_idx)
vloader = D.DataLoader(valid_ds, batch_size=BATCH_SIZE,
shuffle=False, collate_fn = collate_fn,num_workers=0)
code where i initialize StratifiedKFold
df_size = train_csv.shape[0]
num_bins = int(np.floor(1 + np.log2(df_size)))
# bin targets
y = pd.cut(train_csv["target"], bins=num_bins, labels=False)
cv = StratifiedKFold(n_splits=N_FOLDS, shuffle=True, random_state=SEED)
code where i create dataset:
class LitDataset(D.Dataset):
def __init__(self,token,meta,target):
self.token = token
self.meta = meta
self.target = target
def __len__(self):
return len(self.token)
def __getitem__(self,idx):
return torch.tensor(self.token[idx].input_ids),torch.tensor(self.token[idx].attention_mask),\
self.meta[idx],self.target[idx]
def collate_fn(batch):
ids,attns,meta,targets = zip(*batch)
ids = pad_sequence(ids, batch_first=True, padding_value=tokenizer.pad_token_id).to(DEVICE)
attns = pad_sequence(attns, batch_first=True,padding_value=tokenizer.pad_token_id).to(DEVICE)
meta =torch.tensor(meta).to(DEVICE)
targets = torch.tensor(targets).float().to(DEVICE)
return ids,attns,meta,targets
ds = LitDataset(train_csv_stat.token.values,train_meta_features,train_y.values)

Could not convert string to float in jupyter notebook

I am trying to make a ML model , but I am having problems with this one feature. The error given is saying , cannot convert string to float. I tried using a convert method but it is still not working. This code tries to make a ml model
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sn
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
crime_data= pd.read_csv('Crime_Data_from_2020_to_Present.csv')
#offense= crime_data['Crm Cd Desc']
myData= crime_data.drop(columns=['DR_NO','Date Rptd','Rpt Dist No','Part 1-2','Crm Cd','Mocodes','Vict Age','Vict Sex','Vict Descent','Premis Desc','Weapon Used Cd','Weapon Desc','Status','Status Desc','Crm Cd 1','Crm Cd 2','Crm Cd 3','Crm Cd 4','Cross Street','Premis Cd'])
myData['DATE OCC'] = myData['DATE OCC'].astype(float)#method for converting not working
X= myData.drop (columns=['AREA NAME']) #input data
y= myData['AREA NAME'] #output data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)
classifier = RandomForestClassifier(n_estimators = 50)
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
y_pred
This is the error I'm getting
ValueError Traceback (most recent call last)
<ipython-input-16-20d49933ca7e> in <module>
9 #offense= crime_data['Crm Cd Desc']
10 myData= crime_data.drop(columns=['DR_NO','Date Rptd','Rpt Dist No','Part 1-2','Crm Cd','Mocodes','Vict Age','Vict Sex','Vict Descent','Premis Desc','Weapon Used Cd','Weapon Desc','Status','Status Desc','Crm Cd 1','Crm Cd 2','Crm Cd 3','Crm Cd 4','Cross Street','Premis Cd'])
---> 11 myData['DATE OCC'] = myData['DATE OCC'].astype(float)#method for converting not working
12
13 X= myData.drop (columns=['AREA NAME']) #input data
~\anaconda3\lib\site-packages\pandas\core\generic.py in astype(self, dtype, copy, errors)
5696 else:
5697 # else, only a single dtype is given
-> 5698 new_data = self._data.astype(dtype=dtype, copy=copy, errors=errors)
5699 return self._constructor(new_data).__finalize__(self)
5700
~\anaconda3\lib\site-packages\pandas\core\internals\managers.py in astype(self, dtype, copy, errors)
580
581 def astype(self, dtype, copy: bool = False, errors: str = "raise"):
--> 582 return self.apply("astype", dtype=dtype, copy=copy, errors=errors)
583
584 def convert(self, **kwargs):
~\anaconda3\lib\site-packages\pandas\core\internals\managers.py in apply(self, f, filter, **kwargs)
440 applied = b.apply(f, **kwargs)
441 else:
--> 442 applied = getattr(b, f)(**kwargs)
443 result_blocks = _extend_blocks(applied, result_blocks)
444
~\anaconda3\lib\site-packages\pandas\core\internals\blocks.py in astype(self, dtype, copy, errors)
623 vals1d = values.ravel()
624 try:
--> 625 values = astype_nansafe(vals1d, dtype, copy=True)
626 except (ValueError, TypeError):
627 # e.g. astype_nansafe can fail on object-dtype of strings
~\anaconda3\lib\site-packages\pandas\core\dtypes\cast.py in astype_nansafe(arr, dtype, copy, skipna)
895 if copy or is_object_dtype(arr) or is_object_dtype(dtype):
896 # Explicit copy, or required since NumPy can't view from / to object.
--> 897 return arr.astype(dtype, copy=True)
898
899 return arr.view(dtype)
ValueError: could not convert string to float: '01/08/2020 12:00:00 AM'
I changed the code to this
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sn
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
crime_data= pd.read_csv('Crime_Data_from_2020_to_Present.csv')
#offense= crime_data['Crm Cd Desc']
myData= crime_data.drop(columns=['DR_NO','Date Rptd','Rpt Dist No','Part 1-2','Crm Cd','Mocodes','Vict Age','Vict Sex','Vict Descent','Premis Desc','Weapon Used Cd','Weapon Desc','Status','Status Desc','Crm Cd 1','Crm Cd 2','Crm Cd 3','Crm Cd 4','Cross Street','Premis Cd'])
myData['DATE OCC'] = pd.to_datetime(myData['DATE OCC'])#method for converting not working
X= myData.drop (columns=['AREA NAME']) #input data
y= myData['AREA NAME'] #output data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)
classifier = RandomForestClassifier(n_estimators = 50)
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
y_pred
But then I get this error. Please help
TypeError Traceback (most recent call last)
<ipython-input-17-18d34976fcb7> in <module>
16 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)
17 classifier = RandomForestClassifier(n_estimators = 50)
---> 18 classifier.fit(X_train, y_train)
19 y_pred = classifier.predict(X_test)
20 y_pred
~\anaconda3\lib\site-packages\sklearn\ensemble\_forest.py in fit(self, X, y, sample_weight)
301 "sparse multilabel-indicator for y is not supported."
302 )
--> 303 X, y = self._validate_data(X, y, multi_output=True,
304 accept_sparse="csc", dtype=DTYPE)
305 if sample_weight is not None:
~\anaconda3\lib\site-packages\sklearn\base.py in _validate_data(self, X, y, reset, validate_separately, **check_params)
430 y = check_array(y, **check_y_params)
431 else:
--> 432 X, y = check_X_y(X, y, **check_params)
433 out = X, y
434
~\anaconda3\lib\site-packages\sklearn\utils\validation.py in inner_f(*args, **kwargs)
71 FutureWarning)
72 kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 73 return f(**kwargs)
74 return inner_f
75
~\anaconda3\lib\site-packages\sklearn\utils\validation.py in check_X_y(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)
794 raise ValueError("y cannot be None")
795
--> 796 X = check_array(X, accept_sparse=accept_sparse,
797 accept_large_sparse=accept_large_sparse,
798 dtype=dtype, order=order, copy=copy,
~\anaconda3\lib\site-packages\sklearn\utils\validation.py in inner_f(*args, **kwargs)
71 FutureWarning)
72 kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 73 return f(**kwargs)
74 return inner_f
75
~\anaconda3\lib\site-packages\sklearn\utils\validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)enter code here
532
533 if all(isinstance(dtype, np.dtype) for dtype in dtypes_orig):
--> 534 dtype_orig = np.result_type(*dtypes_orig)
535
536 if dtype_numeric:
<__array_function__ internals> in result_type(*args, **kwargs)
TypeError: invalid type promotion

There is a column named DATE OCC in your dataset which has date and time mentioned in it. You are getting that error because, your model is expecting float values and that DATE OCC column you have is in object or datetime64 format. So you have to add this code that I mentioned below:
myData['DATE OCC'] = myData['DATE OCC'].astype('datetime64')
myData['day'] = myData['DATE OCC'].dt.day
myData['month'] = myData['DATE OCC'].dt.month
myData['Year'] = myData['DATE OCC'].dt.year
del myData['DATE OCC']
myData = pd.get_dummies(myData)
X= myData.drop(columns=['AREA NAME']) #input data
y= myData['AREA NAME'] #output data

How to pass multiple text columns to Logistic Regression for multi-label classifcation

I am attempting to assign binary values to 10 labels using 3 features, a headline of an article, a summary of the article, and an id of who created the labels. I'm stuck on trying create a model that can accept all 3 fields as input. Currently, it only works if I only pass just one field. I know I am likely messing something up with the tfidvectorizer, but I can't quite figure it out. Any help would be appreciated. The error I receive (full traceback below) is usually
ValueError: Found input variables with inconsistent numbers of samples: [3, 75897].
screenshot of dataframe
import pandas as pd
import numpy as np
import nltk
from nltk.corpus import stopwords
from nltk.stem.snowball import SnowballStemmer
import re
import string
df = pd.read_csv('../data/homework_clean.csv')
emotion_cols = ['emotion_0', 'emotion_1', 'emotion_2', 'emotion_3', 'emotion_4', 'emotion_5', 'emotion_6', 'emotion_7', 'emotion_8', 'emotion_9']
def removeStopWords(sentence):
global re_stop_words
return re_stop_words.sub(" ", sentence)
def stemming(sentence):
stemSentence = ""
for word in sentence.split():
stem = stemmer.stem(word)
stemSentence += stem
stemSentence += " "
stemSentence = stemSentence.strip()
return stemSentence
df['headline'] = df['headline'].str.lower()
df['headline'] = df['headline'].str.replace(r'[^\w\s]+', '')
df['summary'] = df['summary'].str.lower()
df['summary'] = df['summary'].str.replace(r'[^\w\s]+', '')
stop_words = set(stopwords.words('english'))
re_stop_words = re.compile(r"\b(" + "|".join(stop_words) + ")\\W", re.I)
df['headline'] = df['headline'].apply(removeStopWords)
df['summary'] = df['summary'].apply(removeStopWords)
stemmer = SnowballStemmer('english')
df['headline'] = df['headline'].apply(stemming)
df['summary'] = df['summary'].apply(stemming)
from sklearn.model_selection import train_test_split
train, test = train_test_split(df, random_state = 42, test_size = .2, shuffle = True)
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.pipeline import FeatureUnion
vectorizer = FeatureUnion([
('headline', TfidfVectorizer(strip_accents='unicode', analyzer='word', ngram_range=(1,2), norm='l2')),
('summary', TfidfVectorizer(strip_accents='unicode', analyzer='word', ngram_range=(1,2), norm='l2'))])
x_train = train[['headline', 'summary', 'worker_id']]
y_train = train.drop(labels = ['headline', 'summary', 'worker_id'], axis=1)
x_test = test[['headline', 'summary', 'worker_id']]
y_test = test.drop(labels = ['headline', 'summary', 'worker_id'], axis=1)
# IF I only use one feature it works fine.
# x_train = train['headline']
# y_train = train.drop(labels = ['headline', 'summary', 'worker_id'], axis=1)
# x_test = test['headline']
# y_test = test.drop(labels = ['headline', 'summary', 'worker_id'], axis=1)
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.metrics import accuracy_score
from sklearn.metrics import hamming_loss
from sklearn.multiclass import OneVsRestClassifier
OneVsRest_pipeline = Pipeline(steps = [
('featureunion', vectorizer),
('clf', OneVsRestClassifier(LogisticRegression(solver='sag'))),
])
OneVsRest_pipeline.fit(x_train, y_train)
predictions = OneVsRest_pipeline.predict(x_test)
prediction_prob = OneVsRest_pipeline.predict_proba(x_test)
Full Traceback
ValueError Traceback (most recent call last)
<ipython-input-27-6394288c65f8> in <module>
4 ])
5
----> 6 OneVsRest_pipeline.fit(x_train, y_train)
7 predictions = OneVsRest_pipeline.predict(x_test)
8 prediction_prob = OneVsRest_pipeline.predict_proba(x_test)
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\pipeline.py in fit(self, X, y, **fit_params)
354 self._log_message(len(self.steps) - 1)):
355 if self._final_estimator != 'passthrough':
--> 356 self._final_estimator.fit(Xt, y, **fit_params)
357 return self
358
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\multiclass.py in fit(self, X, y)
214 "not %s" % self.label_binarizer_.classes_[i],
215 self.label_binarizer_.classes_[i]])
--> 216 for i, column in enumerate(columns))
217
218 return self
C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py in __call__(self, iterable)
919 # remaining jobs.
920 self._iterating = False
--> 921 if self.dispatch_one_batch(iterator):
922 self._iterating = self._original_iterator is not None
923
C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py in dispatch_one_batch(self, iterator)
757 return False
758 else:
--> 759 self._dispatch(tasks)
760 return True
761
C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py in _dispatch(self, batch)
714 with self._lock:
715 job_idx = len(self._jobs)
--> 716 job = self._backend.apply_async(batch, callback=cb)
717 # A job can complete so quickly than its callback is
718 # called before we get here, causing self._jobs to
C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py in apply_async(self, func, callback)
180 def apply_async(self, func, callback=None):
181 """Schedule a func to be run"""
--> 182 result = ImmediateResult(func)
183 if callback:
184 callback(result)
C:\ProgramData\Anaconda3\lib\site-packages\joblib\_parallel_backends.py in __init__(self, batch)
547 # Don't delay the application, to avoid keeping the input
548 # arguments in memory
--> 549 self.results = batch()
550
551 def get(self):
C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py in __call__(self)
223 with parallel_backend(self._backend, n_jobs=self._n_jobs):
224 return [func(*args, **kwargs)
--> 225 for func, args, kwargs in self.items]
226
227 def __len__(self):
C:\ProgramData\Anaconda3\lib\site-packages\joblib\parallel.py in <listcomp>(.0)
223 with parallel_backend(self._backend, n_jobs=self._n_jobs):
224 return [func(*args, **kwargs)
--> 225 for func, args, kwargs in self.items]
226
227 def __len__(self):
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\multiclass.py in _fit_binary(estimator, X, y, classes)
78 else:
79 estimator = clone(estimator)
---> 80 estimator.fit(X, y)
81 return estimator
82
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\linear_model\logistic.py in fit(self, X, y, sample_weight)
1530
1531 X, y = check_X_y(X, y, accept_sparse='csr', dtype=_dtype, order="C",
-> 1532 accept_large_sparse=solver != 'liblinear')
1533 check_classification_targets(y)
1534 self.classes_ = np.unique(y)
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\validation.py in check_X_y(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)
727 y = y.astype(np.float64)
728
--> 729 check_consistent_length(X, y)
730
731 return X, y
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\validation.py in check_consistent_length(*arrays)
203 if len(uniques) > 1:
204 raise ValueError("Found input variables with inconsistent numbers of"
--> 205 " samples: %r" % [int(l) for l in lengths])
206
207

Sklearn tree classification on catogorical data

im trying to create a simple classification with tree classifier for disease symptoms. i have tried it using sklearn tree classifier.
it gives the following error. both my code and error is there.
Any suggestion ?
import numpy as np
from sklearn import tree
symptoms = [['flat face','poor moro','hypotonia'],['small head','small jaw','overlapping fingers'], ['small eyes','cleft lip','cleft palate']]
lables = [['Trisomy 21'],['Trisomy 18'],['Trisomy 13']]
classify = tree.DecisionTreeClassifier()
classify = classify.fit(symptoms, lables)
it gives the following error
ValueError Traceback (most recent call last)
<ipython-input-25-0f2c956618c2> in <module>
4 lables = [['Trisomy 21'],['Trisomy 18'],['Trisomy 13']]
5 classify = tree.DecisionTreeClassifier()
----> 6 classify = classify.fit(symptoms, lables)
c:\users\admin\appdata\local\programs\python\python36\lib\site-packages\sklearn\tree\tree.py in fit(self, X, y, sample_weight, check_input, X_idx_sorted)
799 sample_weight=sample_weight,
800 check_input=check_input,
--> 801 X_idx_sorted=X_idx_sorted)
802 return self
803
c:\users\admin\appdata\local\programs\python\python36\lib\site-packages\sklearn\tree\tree.py in fit(self, X, y, sample_weight, check_input, X_idx_sorted)
114 random_state = check_random_state(self.random_state)
115 if check_input:
--> 116 X = check_array(X, dtype=DTYPE, accept_sparse="csc")
117 y = check_array(y, ensure_2d=False, dtype=None)
118 if issparse(X):
c:\users\admin\appdata\local\programs\python\python36\lib\site-packages\sklearn\utils\validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
525 try:
526 warnings.simplefilter('error', ComplexWarning)
--> 527 array = np.asarray(array, dtype=dtype, order=order)
528 except ComplexWarning:
529 raise ValueError("Complex data not supported\n"
c:\users\admin\appdata\local\programs\python\python36\lib\site-packages\numpy\core\numeric.py in asarray(a, dtype, order)
499
500 """
--> 501 return array(a, dtype, copy=False, order=order)
502
503
ValueError: could not convert string to float: 'flat face'

You need to use label encoder for encoding your string values. The following will work for your requirement:
import numpy as np
from sklearn import tree
from sklearn.preprocessing import LabelEncoder
symptoms = [['flat face','poor moro','hypotonia'],['small head','small jaw','overlapping fingers'], ['small eyes','cleft lip','cleft palate']]
lables = [['Trisomy 21'],['Trisomy 18'],['Trisomy 13']]
df = pd.concat([pd.DataFrame(symptoms), pd.DataFrame(lables)], axis=1)
x_cols = ['sym1', 'sym2', 'sym3']
y_col = 'target'
df.columns = x_cols + [y_col]
df = df.apply(LabelEncoder().fit_transform)
classify = tree.DecisionTreeClassifier()
classify.fit(df[x_cols].values, df[y_col].values)