pd_get_dummies до сих пор не может преобразовать строку в число с плавающей точкой: ошибка '/ organization / crowdp c -in c' - PullRequest
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pd_get_dummies до сих пор не может преобразовать строку в число с плавающей точкой: ошибка '/ organization / crowdp c -in c'

0 голосов
/ 11 марта 2020

Я думал, pd_get_dummies() специально решает эту проблему. Куда я иду не так?

Попытается опубликовать некоторые данные = invest.head()

funding_rounds  founded_year    seed    venture equity_crowdfunding undisclosed convertible_note    debt_financing  angel   grant   ... last_funding_at_2014-11-21  last_funding_at_2014-11-24  last_funding_at_2014-11-25  last_funding_at_2014-11-26  last_funding_at_2014-11-27  last_funding_at_2014-11-28  last_funding_at_2014-12-01  last_funding_at_2014-12-02  last_funding_at_2014-12-10  last_funding_at_2014-12-24
0   1.0 2012.0  1750000.0   0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0   0   0   0   0   0   0   0   0   0
8   1.0 2010.0  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
12  1.0 1986.0  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

Попробовать базовое c моделирование: 

invest=pd.get_dummies(invest)
X=invest.drop(['funding_total_usd '],axis=1)
y=invest[' funding_total_usd ']
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=1)
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(X_train, y_train)
y_pred = lin_reg.predict(X_test)

, но все равно получите: 

ValueError                                Traceback (most recent call last)
<ipython-input-112-0a9e89083ca4> in <module>
      1 from sklearn.linear_model import LinearRegression
      2 lin_reg = LinearRegression()
----> 3 lin_reg.fit(X_train, y_train)
      4 y_pred = lin_reg.predict(X_test)

~\Anaconda3\lib\site-packages\sklearn\linear_model\base.py in fit(self, X, y, sample_weight)
    461         n_jobs_ = self.n_jobs
    462         X, y = check_X_y(X, y, accept_sparse=['csr', 'csc', 'coo'],
--> 463                          y_numeric=True, multi_output=True)
    464 
    465         if sample_weight is not None and np.atleast_1d(sample_weight).ndim > 1:

~\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)
    717                     ensure_min_features=ensure_min_features,
    718                     warn_on_dtype=warn_on_dtype,
--> 719                     estimator=estimator)
    720     if multi_output:
    721         y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,

~\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, warn_on_dtype, estimator)
    534         # make sure we actually converted to numeric:
    535         if dtype_numeric and array.dtype.kind == "O":
--> 536             array = array.astype(np.float64)
    537         if not allow_nd and array.ndim >= 3:
    538             raise ValueError("Found array with dim %d. %s expected <= 2."

ValueError: could not convert string to float: '/organization/crowdpc-inc
...