diff --git a/.asv/html/index.json b/.asv/html/index.json index 7d3fe28..fb35632 100644 --- a/.asv/html/index.json +++ b/.asv/html/index.json @@ -1 +1 @@ -{"project": "corneto", "project_url": "http://saezlab.github.io/corneto", "show_commit_url": "http://github.com/saezlab/corneto/commit/", "hash_length": 8, "revision_to_hash": {"1": "54c466447c96490c192539a4db5b0f7a23196922", "3": "56da00d1d9e4bd1a9f6e7bab3f54ea9801ad6914", "11": "bbcdf4b854275eb80e4efcc2d5530b0a80cfe26b", "12": "c6b0797aa75f79c11e145bf15e9f5fcfd925cc23", "18": "3b0d4e5ce459ed5d2801f784393977987f035274", "25": "3146fe504dc3bb1d059b740904044e956ba2a18a", "28": "49839a604e8be3f7dc2ba74901ae9b8dc24a897d", "30": "a171596d4d671515c985973a1fadf3ebdbb0d013", "49": "6baeec3a41c11bd6821f0d84a365a56b26712f53", "61": "9b65bca341c94c453b003f2573e764c3a7b5bcb6", "68": "05e6da0edc5cd505f478f7ca580e74977734af9b", "69": "9283fbe8e7e9a149809fd078d9fbffe5179eb399", "70": "dbfce02d9677fbd2738710022ef4622a3db51350", "74": 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setup(self):\n self.d = {}\n for x in range(500):\n self.d[x] = None", "min_run_count": 2, "name": "benchmarks.TimeSuite.time_keys", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "86e015a3c40c52da31e4185fff7c7176c38c5e1e1e4aba71912db0b388225191", "warmup_time": -1}, "benchmarks.TimeSuite.time_range": {"code": "class TimeSuite:\n def time_range(self):\n d = self.d\n for key in range(500):\n d[key]\n\n def setup(self):\n self.d = {}\n for x in range(500):\n self.d[x] = None", "min_run_count": 2, "name": "benchmarks.TimeSuite.time_range", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "72a6802b5ba2270cc4af88ddbd67e3c44fe4f4c8b292257438a394167fbb158b", "warmup_time": -1}, "benchmarks.TimeSuite.time_values": {"code": "class TimeSuite:\n def time_values(self):\n for value in self.d.values():\n pass\n\n def setup(self):\n self.d = {}\n for x in range(500):\n self.d[x] = None", "min_run_count": 2, "name": "benchmarks.TimeSuite.time_values", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "cace9f2d006aa364dcf5762823afba3680c2fb50179ee30beaf880c0697256e2", "warmup_time": -1}}, "machines": {"laptop": {"arch": "AMD64", "cpu": "11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz", "machine": "laptop", "num_cpu": "8", "os": "Windows 10", "ram": "16GB", "version": 1}}, "tags": {"0.9.0-alpha.0": 1, "0.9.0-alpha.1": 3, "0.9.0-alpha.2": 11, "0.9.0-alpha.3": 12, "0.9.0-alpha.4": 18, "0.9.0-alpha.5": 25, "0.9.0-alpha.6": 28, "0.9.0-alpha.7": 30, "0.9.1-alpha.0": 49, "0.9.1-alpha.1": 61, "0.9.1-alpha.2": 68, "0.9.1-alpha.3": 69, "0.9.1-alpha.4": 70, "0.9.1-alpha.5": 74, "1.0.0.dev0": 288, "1.0.0-alpha": 290}, "pages": [["", "Grid view", "Display as a agrid"], ["summarylist", "List view", "Display as a list"], ["regressions", "Show regressions", "Display information about recent regressions"]]} \ No newline at end of file +{"project": "corneto", "project_url": "http://saezlab.github.io/corneto", "show_commit_url": "http://github.com/saezlab/corneto/commit/", "hash_length": 8, "revision_to_hash": {"1": "54c466447c96490c192539a4db5b0f7a23196922", "3": "56da00d1d9e4bd1a9f6e7bab3f54ea9801ad6914", "11": "bbcdf4b854275eb80e4efcc2d5530b0a80cfe26b", "12": "c6b0797aa75f79c11e145bf15e9f5fcfd925cc23", "18": "3b0d4e5ce459ed5d2801f784393977987f035274", "25": "3146fe504dc3bb1d059b740904044e956ba2a18a", "28": "49839a604e8be3f7dc2ba74901ae9b8dc24a897d", "30": "a171596d4d671515c985973a1fadf3ebdbb0d013", "49": "6baeec3a41c11bd6821f0d84a365a56b26712f53", "61": "9b65bca341c94c453b003f2573e764c3a7b5bcb6", "68": "05e6da0edc5cd505f478f7ca580e74977734af9b", "69": "9283fbe8e7e9a149809fd078d9fbffe5179eb399", "70": "dbfce02d9677fbd2738710022ef4622a3db51350", "74": "816423f166089b8fe2931d15724301a06af4c945", "288": "f9f2baa76ff7d50e79d5c5f6914a8a0bc01594c2", "290": "260a6719fd2714792b094624d99391ec50935fda"}, "revision_to_date": {"1": 1660130961000, "3": 1660733176000, "11": 1661269385000, "12": 1661271467000, "18": 1661851384000, "25": 1663329924000, "28": 1664554303000, "30": 1674468452000, "49": 1676046121000, "61": 1680768995000, "68": 1689886315000, "69": 1689930727000, "70": 1695803487000, "74": 1699872477000, "288": 1726578506000, "290": 1726644816000}, "params": {"branch": ["main", "dev"]}, "graph_param_list": [], "benchmarks": {"benchmarks.MemSuite.mem_list": {"code": "class MemSuite:\n def mem_list(self):\n return [0] * 256", "name": "benchmarks.MemSuite.mem_list", "param_names": [], "params": [], "type": "memory", "unit": "bytes", "version": "abe86bab484550a681c61d7bb6eb081fbd37eb6d5009eba1df85c935530577b9"}, "benchmarks.TimeSuite.time_keys": {"code": "class TimeSuite:\n def time_keys(self):\n for key in self.d.keys():\n pass\n\n def setup(self):\n self.d = {}\n for x in range(500):\n self.d[x] = None", "min_run_count": 2, "name": "benchmarks.TimeSuite.time_keys", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "86e015a3c40c52da31e4185fff7c7176c38c5e1e1e4aba71912db0b388225191", "warmup_time": -1}, "benchmarks.TimeSuite.time_range": {"code": "class TimeSuite:\n def time_range(self):\n d = self.d\n for key in range(500):\n d[key]\n\n def setup(self):\n self.d = {}\n for x in range(500):\n self.d[x] = None", "min_run_count": 2, "name": "benchmarks.TimeSuite.time_range", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "72a6802b5ba2270cc4af88ddbd67e3c44fe4f4c8b292257438a394167fbb158b", "warmup_time": -1}, "benchmarks.TimeSuite.time_values": {"code": "class TimeSuite:\n def time_values(self):\n for value in self.d.values():\n pass\n\n def setup(self):\n self.d = {}\n for x in range(500):\n self.d[x] = None", "min_run_count": 2, "name": "benchmarks.TimeSuite.time_values", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "type": "time", "unit": "seconds", "version": "cace9f2d006aa364dcf5762823afba3680c2fb50179ee30beaf880c0697256e2", "warmup_time": -1}}, "machines": {"laptop": {"arch": "AMD64", "cpu": "11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz", "machine": "laptop", "num_cpu": "8", "os": "Windows 10", "ram": "16GB", "version": 1}}, "tags": {"0.9.0-alpha.0": 1, "0.9.0-alpha.1": 3, "0.9.0-alpha.2": 11, "0.9.0-alpha.3": 12, "0.9.0-alpha.4": 18, "0.9.0-alpha.5": 25, "0.9.0-alpha.6": 28, "0.9.0-alpha.7": 30, "0.9.1-alpha.0": 49, "0.9.1-alpha.1": 61, "0.9.1-alpha.2": 68, "0.9.1-alpha.3": 69, "0.9.1-alpha.4": 70, "0.9.1-alpha.5": 74, "1.0.0.dev0": 288, "1.0.0-alpha": 290}, "pages": [["", "Grid view", "Display as a agrid"], ["summarylist", "List view", "Display as a list"], ["regressions", "Show regressions", "Display information about recent regressions"]]} diff --git a/.asv/html/info.json b/.asv/html/info.json index ebf8945..a66e9e4 100644 --- a/.asv/html/info.json +++ b/.asv/html/info.json @@ -1,4 +1,4 @@ { "asv-version": "0.6.4", "timestamp": 1728862093918 -} \ No newline at end of file +} diff --git a/.asv/html/regressions.json b/.asv/html/regressions.json index 37090c9..5a34916 100644 --- a/.asv/html/regressions.json +++ b/.asv/html/regressions.json @@ -1 +1 @@ -{"regressions": []} \ No newline at end of file +{"regressions": []} diff --git a/.asv/html/regressions.xml b/.asv/html/regressions.xml index ce42a89..553fa5d 100644 --- a/.asv/html/regressions.xml +++ b/.asv/html/regressions.xml @@ -1,2 +1,2 @@ -tag:corneto.asv,1970-01-01:/9786c7b0ede6801a54a78836c115d06c01b9fcad359ba9686c040c9be8721be8Airspeed Velocitycorneto performance regressions2024-10-13T23:28:13Z \ No newline at end of file +tag:corneto.asv,1970-01-01:/9786c7b0ede6801a54a78836c115d06c01b9fcad359ba9686c040c9be8721be8Airspeed Velocitycorneto performance regressions2024-10-13T23:28:13Z diff --git a/.asv/results/benchmarks.json b/.asv/results/benchmarks.json index e8542af..569ab5e 100644 --- a/.asv/results/benchmarks.json +++ b/.asv/results/benchmarks.json @@ -105,4 +105,4 @@ "warmup_time": -1 }, "version": 2 -} \ No newline at end of file +} diff --git a/.asv/results/laptop/machine.json b/.asv/results/laptop/machine.json index 22709cb..9226a64 100644 --- a/.asv/results/laptop/machine.json +++ b/.asv/results/laptop/machine.json @@ -6,4 +6,4 @@ "os": "Windows 10", "ram": "16GB", "version": 1 -} \ No newline at end of file +} diff --git a/benchmarks/benchmarks.py b/benchmarks/benchmarks.py index 27df855..8d3603d 100644 --- a/benchmarks/benchmarks.py +++ b/benchmarks/benchmarks.py @@ -1,11 +1,9 @@ import random -from copy import deepcopy -from corneto._graph import Attributes, Graph +from corneto._graph import Graph class TimeSuite: - def setup(self): # Create a graph with at least 1000 edges self.g = Graph() @@ -30,7 +28,7 @@ def time_add_single_edge(self): def time_add_edges(self): self.g = Graph() - edges = [(i, i+1) for i in range(1000)] + edges = [(i, i + 1) for i in range(1000)] self.g.add_edges(edges) def time_bfs(self): diff --git a/corneto/backend/_base.py b/corneto/backend/_base.py index b001da8..cbeaf4b 100644 --- a/corneto/backend/_base.py +++ b/corneto/backend/_base.py @@ -1,9 +1,9 @@ import abc import numbers +import warnings from copy import copy as shallow_copy from numbers import Number from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Tuple, Union -import warnings import numpy as np @@ -1403,8 +1403,7 @@ def __bool__(self) -> bool: return False def __getattr__(self, name): - """ - Intercept any attribute or method call that isn't already defined in the Backend class + """Intercept any attribute or method call that isn't already defined in the Backend class and raise a NotImplementedError. """ if hasattr(super(), name): diff --git a/corneto/methods/method.py b/corneto/methods/method.py index 49f9e85..c6e3156 100644 --- a/corneto/methods/method.py +++ b/corneto/methods/method.py @@ -83,7 +83,7 @@ def build(self, data: List[GraphData]): output_vertices = d.attributes.get("outputs", set()) output_values = list(d.vertex_data.get(v, 0) for v in output_vertices) vertex_indexes = [graph.V.index(key) for key in output_vertices] - #error = self.elementwise_error() + # error = self.elementwise_error() # Getter for the graph @property diff --git a/corneto/utils/_attr.py b/corneto/utils/_attr.py index c73e047..3061ad6 100644 --- a/corneto/utils/_attr.py +++ b/corneto/utils/_attr.py @@ -12,6 +12,7 @@ class Attr(str, Enum): TARGET_ATTR: Represents the key for a target attribute. CUSTOM_ATTR: Represents the key for a custom attribute. """ + VALUE = "__value" EDGE_TYPE = "__edge_type" SOURCE_ATTR = "__source_attr" @@ -35,7 +36,7 @@ def __setattr__(self, __name: str, __value: Any) -> None: Args: __name (str): The name of the attribute to set. - __value (Any): The value to assign to the attribute. If the value is an instance + __value (Any): The value to assign to the attribute. If the value is an instance of `Attr`, its value is used. Raises: @@ -69,7 +70,7 @@ def set_attr(self, key: Attr, value: Any) -> None: Args: key (Attr): The key for the attribute, represented as an `Attr` enum. - value (Any): The value to assign to the key. If the value is an enum, + value (Any): The value to assign to the key. If the value is an enum, its value is used. """ v = value if not isinstance(value, Enum) else value.value @@ -83,7 +84,7 @@ def has_attr(self, key: Attr, value: Optional[Any] = None) -> bool: value (Optional[Any]): The optional value to check for. Returns: - bool: `True` if the attribute exists and matches the value (if provided), + bool: `True` if the attribute exists and matches the value (if provided), otherwise `False`. """ if value is None: diff --git a/docs/tutorials/single-sample-carnival-transcriptomics.ipynb b/docs/tutorials/single-sample-carnival-transcriptomics.ipynb index 4bbafc6..265a335 100644 --- a/docs/tutorials/single-sample-carnival-transcriptomics.ipynb +++ b/docs/tutorials/single-sample-carnival-transcriptomics.ipynb @@ -25,29 +25,30 @@ "metadata": {}, "outputs": [], "source": [ - "# --- Saezlab tools --- \n", + "# --- Saezlab tools ---\n", "# https://decoupler-py.readthedocs.io/\n", + "import gzip\n", + "import os\n", + "import shutil\n", + "import tempfile\n", + "import urllib.request\n", + "\n", "import decoupler as dc\n", + "import numpy as np\n", + "\n", "# https://omnipathdb.org/\n", "import omnipath as op\n", - "# https://saezlab.github.io/\n", - "import corneto as cn\n", - "\n", "\n", - "# --- Additional libs --- \n", + "# Additional packages\n", + "import pandas as pd\n", "\n", + "# --- Additional libs ---\n", "# Pydeseq for differential expression analysis\n", - "from pydeseq2.dds import DeseqDataSet, DefaultInference\n", + "from pydeseq2.dds import DefaultInference, DeseqDataSet\n", "from pydeseq2.ds import DeseqStats\n", "\n", - "# Additional packages\n", - "import pandas as pd\n", - "import numpy as np\n", - "import urllib.request\n", - "import gzip\n", - "import shutil\n", - "import tempfile\n", - "import os" + "# https://saezlab.github.io/\n", + "import corneto as cn" ] }, { @@ -296,25 +297,25 @@ ], "source": [ "# We need to download the dataset, available at GEO GSE151251\n", - "url = 'https://www.ncbi.nlm.nih.gov/geo/download/?acc=GSE151251&format=file&file=GSE151251%5FHSCs%5FCtrl%2Evs%2EHSCs%5FTGFb%2Ecounts%2Etsv%2Egz'\n", + "url = \"https://www.ncbi.nlm.nih.gov/geo/download/?acc=GSE151251&format=file&file=GSE151251%5FHSCs%5FCtrl%2Evs%2EHSCs%5FTGFb%2Ecounts%2Etsv%2Egz\"\n", "\n", - "adata = None \n", + "adata = None\n", "with tempfile.TemporaryDirectory() as tmpdirname:\n", " # Path for the gzipped file in the temp folder\n", - " gz_file_path = os.path.join(tmpdirname, 'counts.txt.gz')\n", - " \n", + " gz_file_path = os.path.join(tmpdirname, \"counts.txt.gz\")\n", + "\n", " # Download the file\n", " with urllib.request.urlopen(url) as response:\n", - " with open(gz_file_path, 'wb') as out_file:\n", + " with open(gz_file_path, \"wb\") as out_file:\n", " shutil.copyfileobj(response, out_file)\n", - " \n", + "\n", " # Decompress the file\n", " decompressed_file_path = gz_file_path[:-3] # Removing '.gz' extension\n", - " with gzip.open(gz_file_path, 'rb') as f_in:\n", - " with open(decompressed_file_path, 'wb') as f_out:\n", + " with gzip.open(gz_file_path, \"rb\") as f_in:\n", + " with open(decompressed_file_path, \"wb\") as f_out:\n", " shutil.copyfileobj(f_in, f_out)\n", "\n", - " adata = pd.read_csv(decompressed_file_path, index_col=2, sep='\\t').iloc[:, 5:].T\n", + " adata = pd.read_csv(decompressed_file_path, index_col=2, sep=\"\\t\").iloc[:, 5:].T\n", "\n", "adata" ] @@ -447,10 +448,12 @@ ], "source": [ "# Process treatment information\n", - "adata.obs['condition'] = ['control' if '-Ctrl' in sample_id else 'treatment' for sample_id in adata.obs.index]\n", + "adata.obs[\"condition\"] = [\n", + " \"control\" if \"-Ctrl\" in sample_id else \"treatment\" for sample_id in adata.obs.index\n", + "]\n", "\n", "# Process sample information\n", - "adata.obs['sample_id'] = [sample_id.split('_')[0] for sample_id in adata.obs.index]\n", + "adata.obs[\"sample_id\"] = [sample_id.split(\"_\")[0] for sample_id in adata.obs.index]\n", "\n", "# Visualize metadata\n", "adata.obs" @@ -476,7 +479,9 @@ ], "source": [ "# Obtain genes that pass the thresholds\n", - "genes = dc.filter_by_expr(adata, group='condition', min_count=10, min_total_count=15, large_n=1, min_prop=1)\n", + "genes = dc.filter_by_expr(\n", + " adata, group=\"condition\", min_count=10, min_total_count=15, large_n=1, min_prop=1\n", + ")\n", "\n", "# Filter by these genes\n", "adata = adata[:, genes].copy()\n", @@ -495,7 +500,7 @@ "inference = DefaultInference()\n", "dds = DeseqDataSet(\n", " adata=adata,\n", - " design_factors='condition',\n", + " design_factors=\"condition\",\n", " refit_cooks=True,\n", " inference=inference,\n", ")" @@ -593,9 +598,7 @@ ], "source": [ "stat_res = DeseqStats(\n", - " dds,\n", - " contrast=[\"condition\", 'treatment', 'control'],\n", - " inference=inference\n", + " dds, contrast=[\"condition\", \"treatment\", \"control\"], inference=inference\n", ")\n", "\n", "stat_res.summary()" @@ -953,7 +956,7 @@ ], "source": [ "# Retrieve CollecTRI gene regulatory network (through Omnipath)\n", - "collectri = dc.get_collectri(organism='human', split_complexes=False)\n", + "collectri = dc.get_collectri(organism=\"human\", split_complexes=False)\n", "collectri" ] }, @@ -1062,7 +1065,7 @@ } ], "source": [ - "mat = results_df[['stat']].T.rename(index={'stat': 'treatment.vs.control'})\n", + "mat = results_df[[\"stat\"]].T.rename(index={\"stat\": \"treatment.vs.control\"})\n", "mat" ] }, @@ -1198,11 +1201,7 @@ ], "source": [ "dc.plot_barplot(\n", - " acts=tf_acts,\n", - " contrast='treatment.vs.control',\n", - " top=25,\n", - " vertical=True,\n", - " figsize=(3, 6)\n", + " acts=tf_acts, contrast=\"treatment.vs.control\", top=25, vertical=True, figsize=(3, 6)\n", ")" ] }, @@ -1226,7 +1225,11 @@ "source": [ "# We obtain ligand-receptor interactions from Omnipath, and we keep only the receptors\n", "# This is our list of a prior potential receptors from which we will infer the network\n", - "unique_receptors = set(op.interactions.LigRecExtra.get(genesymbols=True)[\"target_genesymbol\"].values.tolist())\n", + "unique_receptors = set(\n", + " op.interactions.LigRecExtra.get(genesymbols=True)[\n", + " \"target_genesymbol\"\n", + " ].values.tolist()\n", + ")\n", "len(unique_receptors)" ] }, @@ -1806,8 +1809,8 @@ ], "source": [ "# We get only interactions from SIGNOR http://signor.uniroma2.it/\n", - "pkn = op.interactions.OmniPath.get(databases=['SIGNOR'], genesymbols=True)\n", - "pkn = pkn[pkn.consensus_direction==True]\n", + "pkn = op.interactions.OmniPath.get(databases=[\"SIGNOR\"], genesymbols=True)\n", + "pkn = pkn[pkn.consensus_direction == True]\n", "pkn.head()" ] }, @@ -1938,7 +1941,9 @@ } ], "source": [ - "pkn[\"interaction\"] = pkn[\"is_stimulation\"].astype(int) - pkn[\"is_inhibition\"].astype(int)\n", + "pkn[\"interaction\"] = pkn[\"is_stimulation\"].astype(int) - pkn[\"is_inhibition\"].astype(\n", + " int\n", + ")\n", "sel_pkn = pkn[[\"source_genesymbol\", \"interaction\", \"target_genesymbol\"]]\n", "sel_pkn" ] @@ -1962,7 +1967,9 @@ ], "source": [ "# We create the CORNETO graph by importing the edges and interaction\n", - "G = cn.Graph.from_sif_tuples([(r[0], r[1], r[2]) for _, r in sel_pkn.iterrows() if r[1] != 0])\n", + "G = cn.Graph.from_sif_tuples(\n", + " [(r[0], r[1], r[2]) for _, r in sel_pkn.iterrows() if r[1] != 0]\n", + ")\n", "G.shape" ] }, @@ -2070,7 +2077,11 @@ ], "source": [ "# As measurements, we take the estimated TFs, we will filter out TFs with p-val > 0.001\n", - "significant_tfs = tf_acts[tf_pvals <= 0.001].T.dropna().sort_values(by=\"treatment.vs.control\", ascending=False)\n", + "significant_tfs = (\n", + " tf_acts[tf_pvals <= 0.001]\n", + " .T.dropna()\n", + " .sort_values(by=\"treatment.vs.control\", ascending=False)\n", + ")\n", "significant_tfs" ] }, @@ -2154,7 +2165,9 @@ ], "source": [ "# We keep only the ones in the PKN graph\n", - "measurements = significant_tfs.loc[significant_tfs.index.intersection(G.V)].to_dict()[\"treatment.vs.control\"]\n", + "measurements = significant_tfs.loc[significant_tfs.index.intersection(G.V)].to_dict()[\n", + " \"treatment.vs.control\"\n", + "]\n", "measurements" ] }, @@ -2214,6 +2227,7 @@ "# We prune the network from inputs (receptors) to TFs to improve the performance.\n", "# The pruning step removes only parts of the network that cannot be reached from the pre-selected receptors\n", "from corneto.methods.carnival import preprocess_graph\n", + "\n", "Gp, inputs_p, measurements_p = preprocess_graph(G, inputs, measurements)\n", "Gp.shape" ] @@ -2257,7 +2271,7 @@ "# We create a CARNIVAL problem\n", "P = milp_carnival(Gp, inputs_p, measurements_p, beta_weight=0.2)\n", "\n", - "# The CARNIVAL problem contains useful variables that we will estimate from the data \n", + "# The CARNIVAL problem contains useful variables that we will estimate from the data\n", "P.expr" ] }, @@ -3544,7 +3558,7 @@ "# We find the value for the variables using the solver GUROBI\n", "# (needs to be installed with a valid license, academic licenses are free)\n", "# TimeLimit and Seed are GUROBI specific parameters\n", - "P.solve(solver=\"GUROBI\", Seed=seed);\n", + "P.solve(solver=\"GUROBI\", Seed=seed)\n", "\n", "# We extract the selected edges\n", "G_sol = Gp.edge_subgraph(np.flatnonzero(P.expr.edge_values.value))\n", @@ -3553,7 +3567,11 @@ "values = P.expr.vertex_values.value\n", "vertex_values = {v: values[i] for i, v in enumerate(Gp.V)}\n", "vertex_sol_values = [vertex_values[v] for v in G_sol.V]\n", - "G_sol.plot(custom_vertex_attr=cn.pl.create_graphviz_vertex_attributes(G_sol.V, vertex_sol_values))" + "G_sol.plot(\n", + " custom_vertex_attr=cn.pl.create_graphviz_vertex_attributes(\n", + " G_sol.V, vertex_sol_values\n", + " )\n", + ")" ] }, { @@ -3573,7 +3591,7 @@ ], "source": [ "# Print the values of the objectives:\n", - "# - First objective is error (non-fited TFs). \n", + "# - First objective is error (non-fited TFs).\n", "# - Second objective is number of interactions\n", "for o in P.objectives:\n", " print(o.value)" @@ -4850,7 +4868,11 @@ "values = P.expr.vertex_values.value\n", "vertex_values = {v: values[i] for i, v in enumerate(Gp.V)}\n", "vertex_sol_values = [vertex_values[v] for v in G_sol.V]\n", - "G_sol.plot(custom_vertex_attr=cn.pl.create_graphviz_vertex_attributes(G_sol.V, vertex_sol_values))" + "G_sol.plot(\n", + " custom_vertex_attr=cn.pl.create_graphviz_vertex_attributes(\n", + " G_sol.V, vertex_sol_values\n", + " )\n", + ")" ] }, { @@ -4870,7 +4892,7 @@ ], "source": [ "# Print the values of the objectives:\n", - "# - First objective is error (non-fited TFs). \n", + "# - First objective is error (non-fited TFs).\n", "# - Second objective is number of interactions\n", "for o in P.objectives:\n", " print(o.value)" @@ -5073,7 +5095,10 @@ } ], "source": [ - "df_lowly_abundant_genes = results_df[(results_df.baseMean <= results_df.baseMean.quantile(0.25)) & (results_df.padj >= 0.05)]\n", + "df_lowly_abundant_genes = results_df[\n", + " (results_df.baseMean <= results_df.baseMean.quantile(0.25))\n", + " & (results_df.padj >= 0.05)\n", + "]\n", "df_lowly_abundant_genes.sort_values(by=\"padj\")" ] }, @@ -5226,7 +5251,9 @@ "# Now we will add a penalty to avoid selecting lowly expressed genes\n", "vertices = Gp.V\n", "penalties = np.zeros(Gp.num_vertices)\n", - "penalties[[vertices.index(v) for v in lowly_abundant_genes.intersection(vertices)]] = 0.01\n", + "penalties[[vertices.index(v) for v in lowly_abundant_genes.intersection(vertices)]] = (\n", + " 0.01\n", + ")\n", "penalties" ] }, @@ -5256,12 +5283,12 @@ "\n", "# We multiply the vector variable of selected proteins and the penalties to\n", "# sum the total cost: sum (v1 * penalty1 + v2 * penalty2, + v3 ...) and we add this as an objective\n", - "penalty_vertices = protein_selected @ penalties \n", + "penalty_vertices = protein_selected @ penalties\n", "P.add_objectives(penalty_vertices)\n", "\n", "# Select only 1 receptor\n", "protein_selected = P.expr.vertex_activated + P.expr.vertex_inhibited\n", - "#P += sum(protein_selected[idx_receptors]) == 1\n", + "# P += sum(protein_selected[idx_receptors]) == 1\n", "\n", "P.solve(solver=\"GUROBI\", Seed=seed, TimeLimit=max_time);" ] @@ -6553,7 +6580,11 @@ "values = P.expr.vertex_values.value\n", "vertex_values = {v: values[i] for i, v in enumerate(Gp.V)}\n", "vertex_sol_values = [vertex_values[v] for v in G_sol.V]\n", - "G_sol.plot(custom_vertex_attr=cn.pl.create_graphviz_vertex_attributes(G_sol.V, vertex_sol_values))" + "G_sol.plot(\n", + " custom_vertex_attr=cn.pl.create_graphviz_vertex_attributes(\n", + " G_sol.V, vertex_sol_values\n", + " )\n", + ")" ] }, { @@ -7913,7 +7944,11 @@ "values = P.expr.vertex_values.value\n", "vertex_values = {v: values[i] for i, v in enumerate(Gp.V)}\n", "vertex_sol_values = [vertex_values[v] for v in G_sol.V]\n", - "G_sol.plot(custom_vertex_attr=cn.pl.create_graphviz_vertex_attributes(G_sol.V, vertex_sol_values))" + "G_sol.plot(\n", + " custom_vertex_attr=cn.pl.create_graphviz_vertex_attributes(\n", + " G_sol.V, vertex_sol_values\n", + " )\n", + ")" ] }, { @@ -7926,7 +7961,7 @@ "# Biasing the networks towards activatory interactions\n", "\n", "# Create the carnival problem with Beta = 0 (to not equally penalise all interactions)\n", - "#Gp, inputs_p, measurements_p = preprocess_graph(G, {\"TGFB1\": 0}, measurements)\n", + "# Gp, inputs_p, measurements_p = preprocess_graph(G, {\"TGFB1\": 0}, measurements)\n", "Gp, inputs_p, measurements_p = preprocess_graph(G, inputs, measurements)\n", "P = milp_carnival(Gp, inputs_p, measurements_p, beta_weight=0)\n", "\n", @@ -9269,7 +9304,11 @@ "values = P.expr.vertex_values.value\n", "vertex_values = {v: values[i] for i, v in enumerate(Gp.V)}\n", "vertex_sol_values = [vertex_values[v] for v in G_sol.V]\n", - "G_sol.plot(custom_vertex_attr=cn.pl.create_graphviz_vertex_attributes(G_sol.V, vertex_sol_values))" + "G_sol.plot(\n", + " custom_vertex_attr=cn.pl.create_graphviz_vertex_attributes(\n", + " G_sol.V, vertex_sol_values\n", + " )\n", + ")" ] }, { @@ -9359,7 +9398,9 @@ "source": [ "# Since the problem minimises error, we change sign. Upregulated genes get negative score\n", "# so if they are selected, the error decreases. The opposite for downregulated genes\n", - "df_gene_scores = (-results_df.loc[results_df.index.intersection(Gp.V).difference(tf_acts.columns)].stat)\n", + "df_gene_scores = -results_df.loc[\n", + " results_df.index.intersection(Gp.V).difference(tf_acts.columns)\n", + "].stat\n", "dict_scores = df_gene_scores.to_dict()\n", "\n", "df_gene_scores.sort_values()" @@ -9385,7 +9426,7 @@ "source": [ "# Now we will add a penalty to avoid selecting lowly expressed genes\n", "vertices = Gp.V\n", - "scores = np.array([dict_scores.get(v,0) for v in vertices])\n", + "scores = np.array([dict_scores.get(v, 0) for v in vertices])\n", "np.sum(np.abs(scores))" ] }, @@ -9408,7 +9449,7 @@ "# Create the carnival problem\n", "P = milp_carnival(Gp, inputs_p, measurements_p, beta_weight=0.2)\n", "vertex_selected = P.expr.vertex_activated + P.expr.vertex_inhibited\n", - "total_score = vertex_selected @ scores \n", + "total_score = vertex_selected @ scores\n", "P.add_objectives(total_score, weights=1e-3)\n", 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test_indexing_axis1(backend): def test_zero_function(backend): assert backend.zero_function().value == 0 # TODO: Fix this for picos, as it returns (1,1) - #assert backend.zero_function().shape == () + # assert backend.zero_function().shape == () def test_undirected_flow(backend):