Add virtualenv test & fix some warnings (#60)

.travis.yml

@@ -46,20 +46,30 @@ before_install:
 
 # command to install dependencies
 install:
+  - export DEFAULT_VENV=$VIRTUAL_ENV
   - pip install -r requirements-dev.txt
   - pip install -r requirements-extra.txt
-  - pip install coveralls
+  - pip install virtualenv coveralls
+  - virtualenv venv
+  - source venv/bin/activate
+  - pip install -r requirements.txt
+  - pip install pytest pytest-timeout
+  - if [ -z "$DEFAULT_VENV" ]; then deactivate; else source $DEFAULT_VENV/bin/activate; fi
   - python setup.py build_ext -i
 
 # command to run tests
 script:
   mkdir -p build &&
-  pytest --cov-report= --cov-config .coveragerc-tensor --cov=mars --timeout=1500 mars/tensor &&
+  pytest --cov-report= --cov-config .coveragerc-tensor --cov=mars --timeout=1500 -W ignore::PendingDeprecationWarning mars/tensor &&
   mv .coverage build/.coverage.tensor.file &&
-  pytest --cov-report= --cov-config .coveragerc --cov=mars --timeout=1500 --forked
+  pytest --cov-report= --cov-config .coveragerc --cov=mars --timeout=1500 --forked -W ignore::PendingDeprecationWarning
     $(find mars -maxdepth 1 -mindepth 1 -type d -not -path "mars/tensor" -not -path "*__pycache__*") &&
   mv .coverage build/.coverage.main.file &&
-  coverage combine build/ && coverage report --fail-under=80
+  coverage combine build/ && coverage report --fail-under=80 &&
+  export DEFAULT_VENV=$VIRTUAL_ENV &&
+  source venv/bin/activate &&
+  pytest --timeout=1500 mars/tests/test_session.py &&
+  if [ -z "$DEFAULT_VENV" ]; then deactivate; else source $DEFAULT_VENV/bin/activate; fi
 
 after_success:
   - coveralls

mars/session.py

@@ -14,15 +14,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import uuid
-import json
-import time
 import numpy as np
 
-from .api import MarsAPI
-from .scheduler.graph import GraphState
-from .serialize import dataserializer
-
 
 class LocalSession(object):
     def __init__(self):

mars/tensor/execution/indexing.py

@@ -1,13 +1,13 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 # Copyright 1999-2018 Alibaba Group Holding Ltd.
-# 
+#
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
-# 
+#
 #      http://www.apache.org/licenses/LICENSE-2.0
-# 
+#
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
@@ -19,7 +19,7 @@
 
 
 def _slice(ctx, chunk):
-    ctx[chunk.key] = ctx[chunk.inputs[0].key][chunk.op.slices]
+    ctx[chunk.key] = ctx[chunk.inputs[0].key][tuple(chunk.op.slices)]
 
 
 def _index(ctx, chunk):

mars/tensor/execution/optimizes/tests/test_compose.py

@@ -33,7 +33,7 @@ def testCompose(self):
         test compose in build graph and optimize
         """
 
-        """
+        r"""
         graph(@: node, #: composed_node):
 
         @ --> @ --> @   ========>    #     
@@ -51,7 +51,7 @@ def testCompose(self):
         composed_nodes = graph.compose(keys=[chunks[1].key])
         self.assertEqual(len(composed_nodes), 0)
 
-        """
+        r"""
         graph(@: node, #: composed_node):
 
         @             @              @       @
@@ -97,7 +97,7 @@ def testCompose(self):
 
         # test optimizer compose
 
-        """
+        r"""
         graph(@: node, S: Slice Chunk, #: composed_node):
 
         @                   @              @             @
@@ -123,7 +123,7 @@ def testCompose(self):
         composed_nodes = optimizer.compose()
         self.assertTrue(composed_nodes[0].composed == chunks[2:4])
 
-        """
+        r"""
             graph(@: node, S: Slice Chunk, #: composed_node):
 
             @ --> @ --> S --> @  ========>  # --> S --> @
@@ -142,7 +142,7 @@ def testCompose(self):
         self.assertTrue(composed_nodes[0].composed == chunks[:2])
         self.assertTrue(len(composed_nodes) == 1)
 
-        """
+        r"""
             graph(@: node, S: Slice Chunk, #: composed_node):
 
             @ --> @ --> S --> @ --> @   ========>  # --> S --> #

mars/tensor/execution/reduction.py

@@ -1,13 +1,13 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 # Copyright 1999-2018 Alibaba Group Holding Ltd.
-# 
+#
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
-# 
+#
 #      http://www.apache.org/licenses/LICENSE-2.0
-# 
+#
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
@@ -213,8 +213,9 @@ def _handle(ctx, chunk):
                 if xp != np:
                     inds = [xp.asarray(it) for it in inds]
                 inds.insert(axis, local_args)
-                vals = vals[inds]
-                arg = arg[inds]
+                inds_tuple = tuple(inds)
+                vals = vals[inds_tuple]
+                arg = arg[inds_tuple]
                 if keepdims:
                     vals = xp.expand_dims(vals, axis)
                     arg = xp.expand_dims(arg, axis)
@@ -241,7 +242,7 @@ def _handle(ctx, chunk):
                 if xp != np:
                     inds = [xp.asarray(it) for it in inds]
                 inds.insert(axis, local_args)
-                arg = arg[inds]
+                arg = arg[tuple(inds)]
                 if keepdims:
                     arg = xp.expand_dims(arg, axis)
             ctx[chunk.key] = arg
@@ -310,7 +311,7 @@ def _count_nonzero(ctx, chunk):
             slcs = [slice(None)] * chunk.inputs[0].ndim
             for ax in chunk.op.axis:
                 slcs[ax] = np.newaxis
-            nz = xp.asarray(nz)[slcs]
+            nz = xp.asarray(nz)[tuple(slcs)]
 
         ctx[chunk.key] = nz
 

mars/tensor/expressions/arithmetic/abs.py

@@ -34,7 +34,7 @@ def _is_sparse(cls, x):
 
 @infer_dtype(np.abs)
 def abs(x, out=None, where=None, **kwargs):
-    """
+    r"""
     Calculate the absolute value element-wise.
 
     Parameters

mars/tensor/expressions/arithmetic/absolute.py

@@ -34,7 +34,7 @@ def _is_sparse(cls, x):
 
 @infer_dtype(np.absolute)
 def absolute(x, out=None, where=None, **kwargs):
-    """
+    r"""
     Calculate the absolute value element-wise.
 
     Parameters

mars/tensor/expressions/arithmetic/exp.py

@@ -34,7 +34,7 @@ def _is_sparse(cls, x):
 
 @infer_dtype(np.exp)
 def exp(x, out=None, where=None, **kwargs):
-    """
+    r"""
     Calculate the exponential of all elements in the input tensor.
 
     Parameters

mars/tensor/expressions/arithmetic/power.py

@@ -51,7 +51,7 @@ def _is_sparse(cls, x1, x2):
 
 @infer_dtype(np.power)
 def power(x1, x2, out=None, where=None, **kwargs):
-    """
+    r"""
     First tensor elements raised to powers from second tensor, element-wise.
 
     Raise each base in `x1` to the positionally-corresponding power in

mars/tensor/expressions/arithmetic/sign.py

@@ -34,7 +34,7 @@ def _is_sparse(cls, x):
 
 @infer_dtype(np.sign)
 def sign(x, out=None, where=None, **kwargs):
-    """
+    r"""
     Returns an element-wise indication of the sign of a number.
 
     The `sign` function returns ``-1 if x < 0, 0 if x==0, 1 if x > 0``.  nan

mars/tensor/expressions/arithmetic/sinc.py

@@ -34,7 +34,7 @@ def _is_sparse(cls, x):
 
 @infer_dtype(np.sinc)
 def sinc(x, **kwargs):
-    """
+    r"""
     Return the sinc function.
 
     The sinc function is :math:`\\sin(\\pi x)/(\\pi x)`.

mars/tensor/expressions/base/corrcoef.py

@@ -18,14 +18,14 @@
 
 
 def corrcoef(x, y=None, rowvar=True):
-    """
+    r"""
     Return Pearson product-moment correlation coefficients.
 
     Please refer to the documentation for `cov` for more detail.  The
     relationship between the correlation coefficient matrix, `R`, and the
     covariance matrix, `C`, is
 
-    .. math:: R_{ij} = \\frac{ C_{ij} } { \\sqrt{ C_{ii} * C_{jj} } }
+    .. math:: R_{ij} = \frac{ C_{ij} } { \sqrt{ C_{ii} * C_{jj} } }
 
     The values of `R` are between -1 and 1, inclusive.
 

mars/tensor/expressions/linalg/norm.py

@@ -133,7 +133,7 @@ def _norm(r, ord, axis, keepdims):
 
 
 def norm(x, ord=None, axis=None, keepdims=False):
-    """
+    r"""
     Matrix or vector norm.
 
     This function is able to return one of eight different matrix norms,

mars/tensor/expressions/random/beta.py

@@ -34,7 +34,7 @@ def __call__(self, a, b, chunks=None):
 
 
 def beta(random_state, a, b, size=None, chunks=None, gpu=None, **kw):
-    """
+    r"""
     Draw samples from a Beta distribution.
 
     The Beta distribution is a special case of the Dirichlet distribution,

mars/tensor/expressions/random/binomial.py

@@ -34,7 +34,7 @@ def __call__(self, n, p, chunks=None):
 
 
 def binomial(random_state, n, p, size=None, chunks=None, gpu=None, **kw):
-    """
+    r"""
     Draw samples from a binomial distribution.
 
     Samples are drawn from a binomial distribution with specified

mars/tensor/expressions/random/chisquare.py

@@ -34,7 +34,7 @@ def __call__(self, df, chunks=None):
 
 
 def chisquare(random_state, df, size=None, chunks=None, gpu=None, **kw):
-    """
+    r"""
     Draw samples from a chi-square distribution.
 
     When `df` independent random variables, each with standard normal

mars/tensor/expressions/random/dirichlet.py

@@ -72,7 +72,7 @@ def tile(cls, op):
 
 
 def dirichlet(random_state, alpha, size=None, chunks=None, gpu=None, **kw):
-    """
+    r"""
     Draw samples from the Dirichlet distribution.
 
     Draw `size` samples of dimension k from a Dirichlet distribution. A

mars/tensor/expressions/random/exponential.py

@@ -34,7 +34,7 @@ def __call__(self, scale, chunks=None):
 
 
 def exponential(random_state, scale=1.0, size=None, chunks=None, gpu=None, **kw):
-    """
+    r"""
     Draw samples from an exponential distribution.
 
     Its probability density function is

mars/tensor/expressions/random/gamma.py

@@ -34,7 +34,7 @@ def __call__(self, shape, scale, chunks=None):
 
 
 def gamma(random_state, shape, scale=1.0, size=None, chunks=None, gpu=None, **kw):
-    """
+    r"""
     Draw samples from a Gamma distribution.
 
     Samples are drawn from a Gamma distribution with specified parameters,

mars/tensor/expressions/random/gumbel.py

@@ -34,7 +34,7 @@ def __call__(self, loc, scale, chunks=None):
 
 
 def gumbel(random_state, loc=0.0, scale=1.0, size=None, chunks=None, gpu=None, **kw):
-    """
+    r"""
     Draw samples from a Gumbel distribution.
 
     Draw samples from a Gumbel distribution with specified location and

mars/tensor/expressions/random/hypergeometric.py

@@ -34,7 +34,7 @@ def __call__(self, ngood, nbad, nsample, chunks=None):
 
 
 def hypergeometric(random_state, ngood, nbad, nsample, size=None, chunks=None, gpu=None, **kw):
-    """
+    r"""
     Draw samples from a Hypergeometric distribution.
 
     Samples are drawn from a hypergeometric distribution with specified

mars/tensor/expressions/random/laplace.py

@@ -34,7 +34,7 @@ def __call__(self, loc, scale, chunks=None):
 
 
 def laplace(random_state, loc=0.0, scale=1.0, size=None, chunks=None, gpu=None, **kw):
-    """
+    r"""
     Draw samples from the Laplace or double exponential distribution with
     specified location (or mean) and scale (decay).
 

mars/tensor/expressions/random/logistic.py

@@ -34,7 +34,7 @@ def __call__(self, loc, scale, chunks=None):
 
 
 def logistic(random_state, loc=0.0, scale=1.0, size=None, chunks=None, gpu=None, **kw):
-    """
+    r"""
     Draw samples from a logistic distribution.
 
     Samples are drawn from a logistic distribution with specified

mars/tensor/expressions/random/lognormal.py

@@ -34,7 +34,7 @@ def __call__(self, mean, sigma, chunks=None):
 
 
 def lognormal(random_state, mean=0.0, sigma=1.0, size=None, chunks=None, gpu=None, **kw):
-    """
+    r"""
     Draw samples from a log-normal distribution.
 
     Draw samples from a log-normal distribution with specified mean,

mars/tensor/expressions/random/logseries.py

@@ -34,7 +34,7 @@ def __call__(self, p, chunks=None):
 
 
 def logseries(random_state, p, size=None, chunks=None, gpu=None, **kw):
-    """
+    r"""
     Draw samples from a logarithmic series distribution.
 
     Samples are drawn from a log series distribution with specified

mars/tensor/expressions/random/negative_binomial.py

@@ -34,7 +34,7 @@ def __call__(self, n, p, chunks=None):
 
 
 def negative_binomial(random_state, n, p, size=None, chunks=None, gpu=None, **kw):
-    """
+    r"""
     Draw samples from a negative binomial distribution.
 
     Samples are drawn from a negative binomial distribution with specified

mars/tensor/expressions/random/noncentral_chisquare.py

@@ -34,7 +34,7 @@ def __call__(self, df, nonc, chunks=None):
 
 
 def noncentral_chisquare(random_state, df, nonc, size=None, chunks=None, gpu=None, **kw):
-    """
+    r"""
     Draw samples from a noncentral chi-square distribution.
 
     The noncentral :math:`\chi^2` distribution is a generalisation of

mars/tensor/expressions/random/normal.py

@@ -34,7 +34,7 @@ def __call__(self, loc, scale, chunks=None):
 
 
 def normal(random_state, loc=0.0, scale=1.0, size=None, chunks=None, gpu=None, **kw):
-    """
+    r"""
     Draw random samples from a normal (Gaussian) distribution.
 
     The probability density function of the normal distribution, first

mars/tensor/expressions/random/pareto.py

@@ -34,7 +34,7 @@ def __call__(self, a, chunks=None):
 
 
 def pareto(random_state, a, size=None, chunks=None, gpu=None, **kw):
-    """
+    r"""
     Draw samples from a Pareto II or Lomax distribution with
     specified shape.