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Python pandas2ri.ri2py方法代码示例

本文整理汇总了Python中rpy2.robjects.pandas2ri.ri2py方法的典型用法代码示例。如果您正苦于以下问题:Python pandas2ri.ri2py方法的具体用法?Python pandas2ri.ri2py怎么用?Python pandas2ri.ri2py使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在rpy2.robjects.pandas2ri的用法示例。


在下文中一共展示了pandas2ri.ri2py方法的9个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: load_cv

# 需要导入模块: from rpy2.robjects import pandas2ri [as 别名]
# 或者: from rpy2.robjects.pandas2ri import ri2py [as 别名]
def load_cv(self, path):
        set_wd_str = 'setwd("{0}")'.format(os.getcwd())
        ro.r(set_wd_str)
        ro.r('load("{0}")'.format(path))
        self.rf_cv = ro.r["trained.models"]
        if new_pandas_flag:
            # rpy2 is a complete joke of a package
            try:
                # use this way for conversion for bugged rpy2 versions
                self.cv_folds = pandas2ri.ri2py(ro.r["cvFoldDf"])
            except:
                # this should be the correct way to convert
                # but several versions of rpy2 have a bug
                self.cv_folds = ro.r["cvFoldDf"]
        else:
            self.cv_folds = com.convert_robj(ro.r["cvFoldDf"]) 
开发者ID:KarchinLab,项目名称:2020plus,代码行数:18,代码来源:r_random_forest_clf.py

示例2: predict_proba

# 需要导入模块: from rpy2.robjects import pandas2ri [as 别名]
# 或者: from rpy2.robjects.pandas2ri import ri2py [as 别名]
def predict_proba(self, xtest):
        """Predicts the probability for each class.

        Parameters
        ----------
        xtest : pd.DataFrame
            features for test set
        """
        if new_pandas_flag:
            r_xtest = pandas2ri.py2ri(xtest)
        else:
            r_xtest = com.convert_to_r_dataframe(xtest)
        #r_xtest = pandas2ri.ri2py(xtest)
        pred_prob = self.rf_pred_prob(self.rf, r_xtest)
        if new_pandas_flag:
            py_pred_prob = pandas2ri.ri2py(pred_prob)
        else:
            py_pred_prob = com.convert_robj(pred_prob)
            py_pred_prob = py_pred_prob.values
        #py_pred_prob = pandas2ri.ri2py(pred_prob)
        return py_pred_prob 
开发者ID:KarchinLab,项目名称:2020plus,代码行数:23,代码来源:r_random_forest_clf.py

示例3: test_predict_with_pandas_data

# 需要导入模块: from rpy2.robjects import pandas2ri [as 别名]
# 或者: from rpy2.robjects.pandas2ri import ri2py [as 别名]
def test_predict_with_pandas_data(self, Model, dataframe):
        X, y = dataframe
        model = Model(scriptname='myscript', funcname='myfunc', some='kwarg')
        model.r['predict'].return_value = numpy.array(
            [[0.1, 0.2, 0.7], [0.8, 0.1, 0.1]])
        model.fit(X, y)

        result = model.predict(X)
        predictargs = model.r['predict'].call_args
        assert predictargs[0][0] is model.rmodel_
        assert (ri2py(predictargs[0][1]).values == X.values).all()
        assert predictargs[1]['type'] == 'prob'
        assert (result ==
                numpy.argmax(model.r['predict'].return_value, axis=1)).all()

        result = model.predict_proba(X)
        assert (result == model.r['predict'].return_value).all() 
开发者ID:ottogroup,项目名称:palladium,代码行数:19,代码来源:test_R.py

示例4: test_smoke

# 需要导入模块: from rpy2.robjects import pandas2ri [as 别名]
# 或者: from rpy2.robjects.pandas2ri import ri2py [as 别名]
def test_smoke(self, dataset, model):
        X, y = dataset()
        model.fit(X, y)

        assert model.predict(X).shape == (
            ri2py(y).shape if isinstance(y, Vector) else y.shape)
        score_1 = model.score(X, y)
        assert score_1 >= 0.1

        # Convert X to its Python or R equivalent and check if scores
        # match:
        X_t = py2ri(X) if isinstance(X, DataFrame) else ri2py(X)
        score_2 = model.score(X_t, y)
        assert score_2 == score_1

        # Convert X to a Python list and run the prediction:
        X_t2 = ri2py(X) if not isinstance(X, DataFrame) else X
        X_t2 = X_t2.values.tolist()
        score_3 = model.score(X_t2, y)
        assert score_3 == score_1 
开发者ID:ottogroup,项目名称:palladium,代码行数:22,代码来源:test_R.py

示例5: predict

# 需要导入模块: from rpy2.robjects import pandas2ri [as 别名]
# 或者: from rpy2.robjects.pandas2ri import ri2py [as 别名]
def predict(self, xtest):
        """Predicts class via majority vote.

        Parameters
        ----------
        xtest : pd.DataFrame
            features for test set
        """
        if new_pandas_flag:
            r_xtest = pandas2ri.py2ri(xtest)
        else:
            r_xtest = com.convert_to_r_dataframe(xtest)
        #r_xtest = pandas2ri.py2ri(xtest)
        pred = self.rf_pred(self.rf, r_xtest)
        if new_pandas_flag:
            #py_pred = pandas2ri.ri2py(pred)
            tmp_genes = pred[1]
            tmp_pred_class = pred[0]
            genes = pandas2ri.ri2py(tmp_genes)
            pred_class = pandas2ri.ri2py(tmp_pred_class)
        else:
            py_pred = com.convert_robj(pred)
            genes, pred_class = zip(*py_pred.items())
            #genes = com.convert_robj(tmp_genes)
            #pred_class = com.convert_robj(tmp_pred_class)
        tmp_df = pd.DataFrame({'pred_class': pred_class},
                              index=genes)
        tmp_df = tmp_df.reindex(xtest.index)
        tmp_df -= 1  # for some reason the class numbers start at 1
        return tmp_df['pred_class'] 
开发者ID:KarchinLab,项目名称:2020plus,代码行数:32,代码来源:r_random_forest_clf.py

示例6: test_fit_with_pandas_data

# 需要导入模块: from rpy2.robjects import pandas2ri [as 别名]
# 或者: from rpy2.robjects.pandas2ri import ri2py [as 别名]
def test_fit_with_pandas_data(self, Model, dataframe):
        X, y = dataframe
        model = Model(scriptname='myscript', funcname='myfunc', some='kwarg')
        model.fit(X, y)
        funcargs = model.r['myfunc'].call_args
        assert (ri2py(funcargs[0][0]).values == X.values).all()
        assert (ri2py(funcargs[0][1]) == y).all()
        assert funcargs[1]['some'] == 'kwarg' 
开发者ID:ottogroup,项目名称:palladium,代码行数:10,代码来源:test_R.py

示例7: fit

# 需要导入模块: from rpy2.robjects import pandas2ri [as 别名]
# 或者: from rpy2.robjects.pandas2ri import ri2py [as 别名]
def fit(self, xtrain, ytrain):
        """The fit method trains R's random forest classifier.

        NOTE: the method name ("fit") and method signature were choosen
        to be consistent with scikit learn's fit method.

        Parameters
        ----------
        xtrain : pd.DataFrame
            features for training set
        ytrain : pd.DataFrame
            true class labels (as integers) for training set
        """
        label_counts = ytrain.value_counts()
        if self.is_onco_pred and self.is_tsg_pred:
            sampsize = [label_counts[self.other_num],
                        label_counts[self.onco_num],
                        label_counts[self.tsg_num]]
        elif self.is_onco_pred:
            sampsize = [label_counts[self.other_num],
                        label_counts[self.onco_num]]
        elif self.is_tsg_pred:
            sampsize = [label_counts[self.other_num],
                        label_counts[self.tsg_num]]

        self.set_sample_size(sampsize)
        ytrain.index = xtrain.index  # ensure indexes match
        xtrain['true_class'] = ytrain

        # convert
        if new_pandas_flag:
            r_xtrain = pandas2ri.py2ri(xtrain)
        else:
            r_xtrain = com.convert_to_r_dataframe(xtrain)
        #ro.globalenv['trainData'] = r_xtrain
        self.rf = self.rf_fit(r_xtrain, self.ntrees, self.sample_size)
        r_imp = self.rf_imp(self.rf)  # importance dataframe in R
        if new_pandas_flag:
            self.feature_importances_ = pandas2ri.ri2py(r_imp)
        else:
            self.feature_importances_ = com.convert_robj(r_imp)
        #self.feature_importances_ = pandas2ri.ri2py(r_imp) 
开发者ID:KarchinLab,项目名称:2020plus,代码行数:44,代码来源:r_random_forest_clf.py

示例8: convert_r_obj

# 需要导入模块: from rpy2.robjects import pandas2ri [as 别名]
# 或者: from rpy2.robjects.pandas2ri import ri2py [as 别名]
def convert_r_obj(v: Any, obj_to_obj: bool=True, verbose: bool=True) -> Any:
        """Function with manually specified conversion from a r-object to a python object
        """
        if type(v) == ro.rinterface.RNULLType:
            return None
        elif type(v) == ro.vectors.Matrix:
            return np.array(v)
        elif type(v) == ro.vectors.FloatVector:
            return np.array(v, dtype="float64")
        elif type(v) == ro.vectors.IntVector:
            return np.array(v, dtype="int64")
        elif type(v) == ro.rinterface.RNULLType:
            return None
        elif type(v) == ro.vectors.ListVector:
            try:
                return {v.names[i]: convert_r_obj(v[i], obj_to_obj=obj_to_obj) for i in range(len(v))}
            except TypeError:
                return {i: convert_r_obj(v[i], obj_to_obj=obj_to_obj) for i in range(len(v))}
        elif type(v) == ro.vectors.StrVector:
            if len(v) == 1:
                return str(v[0])
            else:
                try:
                    return {v.names[i]: convert_r_obj(v[i], obj_to_obj=obj_to_obj) for i in range(len(v))}
                except TypeError:
                    return {i: convert_r_obj(v[i], obj_to_obj=obj_to_obj) for i in range(len(v))}
        elif type(v) == ro.vectors.DataFrame:
            from rpy2.robjects import pandas2ri
            return pandas2ri.ri2py(v)
        elif type(v) == ro.methods.RS4:
            if obj_to_obj:
                class RS4Object(object):
                    def __repr__(self) -> str:
                        return f"< RS4Object with attributes: {list(self.__dict__.keys())} >"
                rs4obj = RS4Object()
                for k in tuple(v.slotnames()):
                    setattr(rs4obj, k, convert_r_obj(v.slots[k], obj_to_obj=obj_to_obj))
                return rs4obj
            else:
                return {k: convert_r_obj(v.slots[k]) for k in tuple(v.slotnames())}
        else:
            if type(v) != str:
                if verbose:
                    print(f"not supported yet {type(v)}")
            return v 
开发者ID:velocyto-team,项目名称:velocyto.py,代码行数:47,代码来源:r_interface.py

示例9: covarFilter

# 需要导入模块: from rpy2.robjects import pandas2ri [as 别名]
# 或者: from rpy2.robjects.pandas2ri import ri2py [as 别名]
def covarFilter(infile,
                time_points,
                replicates,
                quantile):
    '''
    Filter gene list based on the distribution of the
    sums of the covariance of each gene.  This is highly
    recommended to reduce the total number of genes used
    in the dynamic time warping clustering to reduce the
    computational time.  The threshold is placed at the
    intersection of the expected and observed value
    for the given quantile.
    '''

    time_points.sort()
    time_rep_comb = [x for x in itertools.product(time_points, replicates)]
    time_cond = ro.StrVector([x[0] for x in time_rep_comb])
    rep_cond = ro.StrVector([x[1] for x in time_rep_comb])
    df = pd.read_table(infile, sep="\t", header=0, index_col=0)

    df.drop(['replicates'], inplace=True, axis=1)
    df.drop(['times'], inplace=True, axis=1)
    df = df.fillna(0.0)

    R.assign('diff_data', df)

    E.info("loading data frame")

    # need to be careful about column headers and transposing data frames

    R('''trans_data <- data.frame(diff_data)''')
    R('''times <- c(%s)''' % time_cond.r_repr())
    R('''replicates <- c(%s)''' % rep_cond.r_repr())

    # calculate the covariance matrix for all genes
    # sum each gene's covariance vector

    E.info("calculating sum of covariance of expression")

    R('''covar.mat <- abs(cov(trans_data))''')
    R('''sum.covar <- rowSums(covar.mat)''')
    R('''exp.covar <- abs(qnorm(ppoints(sum.covar),'''
      '''mean=mean(sum.covar), sd=sd(sum.covar)))''')
    R('''sum.covar.quant <- quantile(sum.covar)''')
    R('''exp.covar.quant <- quantile(exp.covar)''')

    E.info("filter on quantile")

    R('''filtered_genes <- names(sum.covar[sum.covar > '''
      '''sum.covar.quant[%(quantile)i]'''
      ''' & sum.covar > exp.covar.quant[%(quantile)i]])''' % locals())
    R('''filtered_frame <- data.frame(diff_data[, filtered_genes],'''
      '''times, replicates)''')

    filtered_frame = pandas2i.ri2py('filtered_frame').T

    return filtered_frame 
开发者ID:CGATOxford,项目名称:CGATPipelines,代码行数:59,代码来源:PipelineTimeseries.py


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