prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  Update lasso example
  **Description**
  The lasso example references heat.ml.regression.lasso
  
  **To Reproduce**
  Steps to reproduce the behavior:
  1. Which module/class/function is affected?
    examples/lasso/demo.py
  2. What are the circumstances under which the bug appears?
    run demo.py
  3. What is the exact error-message/errorous behavious?
    `Traceback (most recent call last):
    File "demo.py", line 10, in <module>
      import heat.ml.regression.lasso as lasso
  ModuleNotFoundError: No module named 'heat.ml'`
  
  **Expected behavior**
  Should import heat.core.regression.lasso
  
  **Version Info**
   Which version are you using?
  0.2.1
  
  Update lasso example
  **Description**
  The lasso example references heat.ml.regression.lasso
  
  **To Reproduce**
  Steps to reproduce the behavior:
  1. Which module/class/function is affected?
    examples/lasso/demo.py
  2. What are the circumstances under which the bug appears?
    run demo.py
  3. What is the exact error-message/errorous behavious?
    `Traceback (most recent call last):
    File "demo.py", line 10, in <module>
      import heat.ml.regression.lasso as lasso
  ModuleNotFoundError: No module named 'heat.ml'`
  
  **Expected behavior**
  Should import heat.core.regression.lasso
  
  **Version Info**
   Which version are you using?
  0.2.1
  
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `examples/lasso/demo.py`
  Content:
  ```
  1 import numpy as np
  2 import torch
  3 import sys
  4 
  5 sys.path.append("../../")
  6 
  7 import heat as ht
  8 from matplotlib import pyplot as plt
  9 from sklearn import datasets
  10 import heat.ml.regression.lasso as lasso
  11 import plotfkt
  12 
  13 # read scikit diabetes data set
  14 diabetes = datasets.load_diabetes()
  15 
  16 # load diabetes dataset from hdf5 file
  17 X = ht.load_hdf5("../../heat/datasets/data/diabetes.h5", dataset="x", split=0)
  18 y = ht.load_hdf5("../../heat/datasets/data/diabetes.h5", dataset="y", split=0)
  19 
  20 # normalize dataset #DoTO this goes into the lasso fit routine soon as issue #106 is solved
  21 X = X / ht.sqrt((ht.mean(X ** 2, axis=0)))
  22 
  23 # HeAT lasso instance
  24 estimator = lasso.HeatLasso(max_iter=100)
  25 
  26 # List  lasso model parameters
  27 theta_list = list()
  28 
  29 # Range of lambda values
  30 lamda = np.logspace(0, 4, 10) / 10
  31 
  32 # compute the lasso path
  33 for l in lamda:
  34     estimator.lam = l
  35     estimator.fit(X, y)
  36     theta_list.append(estimator.theta.numpy().flatten())
  37 
  38 # Stack estimated model parameters into one numpy array
  39 theta_lasso = np.stack(theta_list).T
  40 
  41 # Stack into numpy array
  42 theta_lasso = np.stack(theta_list).T[1:, :]
  43 
  44 
  45 # plot lasso paths
  46 plt.subplot(3, 1, 1)
  47 plotfkt.plot_lasso_path(
  48     lamda, theta_lasso, diabetes.feature_names, title="Lasso Paths - HeAT implementation"
  49 )
  50 
  51 if X.is_distributed():
  52     distributed = X.comm.rank
  53 else:
  54     distributed = False
  55 
  56 # Now the same stuff in numpy
  57 X = diabetes.data.astype("float32")
  58 y = diabetes.target.astype("float32")
  59 
  60 m, _ = X.shape
  61 X = np.concatenate((np.ones((m, 1)).astype("float32"), X), axis=1)
  62 
  63 # normalize dataset
  64 X = X / np.sqrt((np.mean(X ** 2, axis=0)))
  65 
  66 # Numpy lasso instance
  67 estimator = lasso.NumpyLasso(max_iter=100)
  68 
  69 # List  lasso model parameters
  70 theta_list = list()
  71 
  72 # Range of lambda values
  73 lamda = np.logspace(0, 4, 10) / 10
  74 
  75 # compute the lasso path
  76 for l in lamda:
  77     estimator.lam = l
  78     estimator.fit(X, y)
  79     theta_list.append(estimator.theta.flatten())
  80 
  81 # Stack estimated model parameters into one numpy array
  82 theta_lasso = np.stack(theta_list).T
  83 
  84 # Stack into numpy array
  85 theta_lasso = np.stack(theta_list).T[1:, :]
  86 
  87 # plot lasso paths
  88 plt.subplot(3, 1, 2)
  89 plotfkt.plot_lasso_path(
  90     lamda, theta_lasso, diabetes.feature_names, title="Lasso Paths - Numpy implementation"
  91 )
  92 
  93 # Now the same stuff again in PyTorch
  94 X = torch.tensor(X)
  95 y = torch.tensor(y)
  96 
  97 # HeAT lasso instance
  98 estimator = lasso.PytorchLasso(max_iter=100)
  99 
  100 # List lasso model parameters
  101 theta_list = list()
  102 
  103 # Range of lambda values
  104 lamda = np.logspace(0, 4, 10) / 10
  105 
  106 # compute the lasso path
  107 for l in lamda:
  108     estimator.lam = l
  109     estimator.fit(X, y)
  110     theta_list.append(estimator.theta.numpy().flatten())
  111 
  112 # Stack estimated model parameters into one numpy array
  113 theta_lasso = np.stack(theta_list).T
  114 
  115 # Stack into numpy array
  116 theta_lasso = np.stack(theta_list).T[1:, :]
  117 
  118 # plot lasso paths
  119 plt.subplot(3, 1, 3)
  120 plotfkt.plot_lasso_path(
  121     lamda, theta_lasso, diabetes.feature_names, title="Lasso Paths - PyTorch implementation"
  122 )
  123 
  124 # plot only with first rank
  125 if distributed is False:
  126     plt.show()
  127 elif distributed == 0:
  128     plt.show()
  129
  ```
  --- END FILES ---
  
  Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.
  
  Here is an example:
  
  ```diff
  diff --git a/examples/server_async.py b/examples/server_async.py
  --- a/examples/server_async.py
  +++ b/examples/server_async.py
  @@ -313,4 +313,4 @@
   
   
   if __name__ == "__main__":
  -    asyncio.run(run_async_server("."), debug=True)
  +    asyncio.run(run_async_server(), debug=True)
  diff --git a/examples/server_sync.py b/examples/server_sync.py
  --- a/examples/server_sync.py
  +++ b/examples/server_sync.py
  @@ -313,5 +313,5 @@
   
   
   if __name__ == "__main__":
  -    server = run_sync_server(".")
  +    server = run_sync_server()
       server.shutdown()
  
  ```
  
golden_diff: diff --git a/examples/lasso/demo.py b/examples/lasso/demo.py
  --- a/examples/lasso/demo.py
  +++ b/examples/lasso/demo.py
  @@ -7,7 +7,7 @@
   import heat as ht
   from matplotlib import pyplot as plt
   from sklearn import datasets
  -import heat.ml.regression.lasso as lasso
  +import heat.core.regression.lasso as lasso
   import plotfkt
   
   # read scikit diabetes data set
  
verification_info: {"golden_diff": "diff --git a/examples/lasso/demo.py b/examples/lasso/demo.py\n--- a/examples/lasso/demo.py\n+++ b/examples/lasso/demo.py\n@@ -7,7 +7,7 @@\n import heat as ht\n from matplotlib import pyplot as plt\n from sklearn import datasets\n-import heat.ml.regression.lasso as lasso\n+import heat.core.regression.lasso as lasso\n import plotfkt\n \n # read scikit diabetes data set\n", "issue": "Update lasso example\n**Description**\r\nThe lasso example references heat.ml.regression.lasso\r\n\r\n**To Reproduce**\r\nSteps to reproduce the behavior:\r\n1. Which module/class/function is affected?\r\n  examples/lasso/demo.py\r\n2. What are the circumstances under which the bug appears?\r\n  run demo.py\r\n3. What is the exact error-message/errorous behavious?\r\n  `Traceback (most recent call last):\r\n  File \"demo.py\", line 10, in <module>\r\n    import heat.ml.regression.lasso as lasso\r\nModuleNotFoundError: No module named 'heat.ml'`\r\n\r\n**Expected behavior**\r\nShould import heat.core.regression.lasso\r\n\r\n**Version Info**\r\n Which version are you using?\r\n0.2.1\r\n\nUpdate lasso example\n**Description**\r\nThe lasso example references heat.ml.regression.lasso\r\n\r\n**To Reproduce**\r\nSteps to reproduce the behavior:\r\n1. Which module/class/function is affected?\r\n  examples/lasso/demo.py\r\n2. What are the circumstances under which the bug appears?\r\n  run demo.py\r\n3. What is the exact error-message/errorous behavious?\r\n  `Traceback (most recent call last):\r\n  File \"demo.py\", line 10, in <module>\r\n    import heat.ml.regression.lasso as lasso\r\nModuleNotFoundError: No module named 'heat.ml'`\r\n\r\n**Expected behavior**\r\nShould import heat.core.regression.lasso\r\n\r\n**Version Info**\r\n Which version are you using?\r\n0.2.1\r\n\n", "before_files": [{"content": "import numpy as np\nimport torch\nimport sys\n\nsys.path.append(\"../../\")\n\nimport heat as ht\nfrom matplotlib import pyplot as plt\nfrom sklearn import datasets\nimport heat.ml.regression.lasso as lasso\nimport plotfkt\n\n# read scikit diabetes data set\ndiabetes = datasets.load_diabetes()\n\n# load diabetes dataset from hdf5 file\nX = ht.load_hdf5(\"../../heat/datasets/data/diabetes.h5\", dataset=\"x\", split=0)\ny = ht.load_hdf5(\"../../heat/datasets/data/diabetes.h5\", dataset=\"y\", split=0)\n\n# normalize dataset #DoTO this goes into the lasso fit routine soon as issue #106 is solved\nX = X / ht.sqrt((ht.mean(X ** 2, axis=0)))\n\n# HeAT lasso instance\nestimator = lasso.HeatLasso(max_iter=100)\n\n# List  lasso model parameters\ntheta_list = list()\n\n# Range of lambda values\nlamda = np.logspace(0, 4, 10) / 10\n\n# compute the lasso path\nfor l in lamda:\n    estimator.lam = l\n    estimator.fit(X, y)\n    theta_list.append(estimator.theta.numpy().flatten())\n\n# Stack estimated model parameters into one numpy array\ntheta_lasso = np.stack(theta_list).T\n\n# Stack into numpy array\ntheta_lasso = np.stack(theta_list).T[1:, :]\n\n\n# plot lasso paths\nplt.subplot(3, 1, 1)\nplotfkt.plot_lasso_path(\n    lamda, theta_lasso, diabetes.feature_names, title=\"Lasso Paths - HeAT implementation\"\n)\n\nif X.is_distributed():\n    distributed = X.comm.rank\nelse:\n    distributed = False\n\n# Now the same stuff in numpy\nX = diabetes.data.astype(\"float32\")\ny = diabetes.target.astype(\"float32\")\n\nm, _ = X.shape\nX = np.concatenate((np.ones((m, 1)).astype(\"float32\"), X), axis=1)\n\n# normalize dataset\nX = X / np.sqrt((np.mean(X ** 2, axis=0)))\n\n# Numpy lasso instance\nestimator = lasso.NumpyLasso(max_iter=100)\n\n# List  lasso model parameters\ntheta_list = list()\n\n# Range of lambda values\nlamda = np.logspace(0, 4, 10) / 10\n\n# compute the lasso path\nfor l in lamda:\n    estimator.lam = l\n    estimator.fit(X, y)\n    theta_list.append(estimator.theta.flatten())\n\n# Stack estimated model parameters into one numpy array\ntheta_lasso = np.stack(theta_list).T\n\n# Stack into numpy array\ntheta_lasso = np.stack(theta_list).T[1:, :]\n\n# plot lasso paths\nplt.subplot(3, 1, 2)\nplotfkt.plot_lasso_path(\n    lamda, theta_lasso, diabetes.feature_names, title=\"Lasso Paths - Numpy implementation\"\n)\n\n# Now the same stuff again in PyTorch\nX = torch.tensor(X)\ny = torch.tensor(y)\n\n# HeAT lasso instance\nestimator = lasso.PytorchLasso(max_iter=100)\n\n# List lasso model parameters\ntheta_list = list()\n\n# Range of lambda values\nlamda = np.logspace(0, 4, 10) / 10\n\n# compute the lasso path\nfor l in lamda:\n    estimator.lam = l\n    estimator.fit(X, y)\n    theta_list.append(estimator.theta.numpy().flatten())\n\n# Stack estimated model parameters into one numpy array\ntheta_lasso = np.stack(theta_list).T\n\n# Stack into numpy array\ntheta_lasso = np.stack(theta_list).T[1:, :]\n\n# plot lasso paths\nplt.subplot(3, 1, 3)\nplotfkt.plot_lasso_path(\n    lamda, theta_lasso, diabetes.feature_names, title=\"Lasso Paths - PyTorch implementation\"\n)\n\n# plot only with first rank\nif distributed is False:\n    plt.show()\nelif distributed == 0:\n    plt.show()\n", "path": "examples/lasso/demo.py"}], "after_files": [{"content": "import numpy as np\nimport torch\nimport sys\n\nsys.path.append(\"../../\")\n\nimport heat as ht\nfrom matplotlib import pyplot as plt\nfrom sklearn import datasets\nimport heat.core.regression.lasso as lasso\nimport plotfkt\n\n# read scikit diabetes data set\ndiabetes = datasets.load_diabetes()\n\n# load diabetes dataset from hdf5 file\nX = ht.load_hdf5(\"../../heat/datasets/data/diabetes.h5\", dataset=\"x\", split=0)\ny = ht.load_hdf5(\"../../heat/datasets/data/diabetes.h5\", dataset=\"y\", split=0)\n\n# normalize dataset #DoTO this goes into the lasso fit routine soon as issue #106 is solved\nX = X / ht.sqrt((ht.mean(X ** 2, axis=0)))\n\n# HeAT lasso instance\nestimator = lasso.HeatLasso(max_iter=100)\n\n# List  lasso model parameters\ntheta_list = list()\n\n# Range of lambda values\nlamda = np.logspace(0, 4, 10) / 10\n\n# compute the lasso path\nfor l in lamda:\n    estimator.lam = l\n    estimator.fit(X, y)\n    theta_list.append(estimator.theta.numpy().flatten())\n\n# Stack estimated model parameters into one numpy array\ntheta_lasso = np.stack(theta_list).T\n\n# Stack into numpy array\ntheta_lasso = np.stack(theta_list).T[1:, :]\n\n\n# plot lasso paths\nplt.subplot(3, 1, 1)\nplotfkt.plot_lasso_path(\n    lamda, theta_lasso, diabetes.feature_names, title=\"Lasso Paths - HeAT implementation\"\n)\n\nif X.is_distributed():\n    distributed = X.comm.rank\nelse:\n    distributed = False\n\n# Now the same stuff in numpy\nX = diabetes.data.astype(\"float32\")\ny = diabetes.target.astype(\"float32\")\n\nm, _ = X.shape\nX = np.concatenate((np.ones((m, 1)).astype(\"float32\"), X), axis=1)\n\n# normalize dataset\nX = X / np.sqrt((np.mean(X ** 2, axis=0)))\n\n# Numpy lasso instance\nestimator = lasso.NumpyLasso(max_iter=100)\n\n# List  lasso model parameters\ntheta_list = list()\n\n# Range of lambda values\nlamda = np.logspace(0, 4, 10) / 10\n\n# compute the lasso path\nfor l in lamda:\n    estimator.lam = l\n    estimator.fit(X, y)\n    theta_list.append(estimator.theta.flatten())\n\n# Stack estimated model parameters into one numpy array\ntheta_lasso = np.stack(theta_list).T\n\n# Stack into numpy array\ntheta_lasso = np.stack(theta_list).T[1:, :]\n\n# plot lasso paths\nplt.subplot(3, 1, 2)\nplotfkt.plot_lasso_path(\n    lamda, theta_lasso, diabetes.feature_names, title=\"Lasso Paths - Numpy implementation\"\n)\n\n# Now the same stuff again in PyTorch\nX = torch.tensor(X)\ny = torch.tensor(y)\n\n# HeAT lasso instance\nestimator = lasso.PytorchLasso(max_iter=100)\n\n# List lasso model parameters\ntheta_list = list()\n\n# Range of lambda values\nlamda = np.logspace(0, 4, 10) / 10\n\n# compute the lasso path\nfor l in lamda:\n    estimator.lam = l\n    estimator.fit(X, y)\n    theta_list.append(estimator.theta.numpy().flatten())\n\n# Stack estimated model parameters into one numpy array\ntheta_lasso = np.stack(theta_list).T\n\n# Stack into numpy array\ntheta_lasso = np.stack(theta_list).T[1:, :]\n\n# plot lasso paths\nplt.subplot(3, 1, 3)\nplotfkt.plot_lasso_path(\n    lamda, theta_lasso, diabetes.feature_names, title=\"Lasso Paths - PyTorch implementation\"\n)\n\n# plot only with first rank\nif distributed is False:\n    plt.show()\nelif distributed == 0:\n    plt.show()\n", "path": "examples/lasso/demo.py"}]}